1

PA211: Referral methods, client information,
and their impact on unmet client needs.

Cassandra M. Clemency
Haverlack School of Business, Slippery Rock University
ECON-318: Introduction to Econometrics
Under the supervision of Dr. Xintong Wang
December 08, 2025

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Abstract
This paper analyzes and documents the relationships between several relevant client statuses, and
the unmet needs of those clients: Pennsylvania residents who contact PA211 for assistance. My
literature review suggested pronounced disparities in urban-rural food security, and
methodological challenges created by gendered behavioral differences and mental health
problems among clients. Data provided by PA211 uses a three-tier structure, containing unique
IDs to differentiate client interactions from referrals, categorizations of services provided, as well
as a numbering system to allow counting of needs. Unmet needs, or services which clients were
unable to find assistance with after contacting PA211, intersect in varying ways with client
demographic information, repeat-client status, location, and timing. Using multiple and logistic
regression, a stacked bar chart, and a correlation matrix, I analyze and visualize these
relationships. Findings reveal that age is the most significant predictor of differing unmet need
composition and volume; gender, race, income, and veteran status are only minor. Repeat clients
disproportionately seek assistance in housing, while first-time clients seek utility assistance,
suggesting that repeat clients contend with long-term deprivation. Further, rural and urban
categorization of location is insufficient for meaningful analysis, with county-level examination
proving superior, in some cases. This research outlines flaws in PA211’s data procurement
process, including geographic categorization (rural/urban versus county-level) and survey
strategies. I suggest remedies including more rigid surveying of client assets, housing and
employment status, as well as closer examination of heightened unmet needs among those who
fail to divulge ethnic identity.

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Executive Summary
My analysis determined that the most prominent demographic predictor is age, exhibiting a
heightened volume and more varied composition of unmet needs, especially among the 41-60 age group.
Gender showed mixed results, with female clients exhibiting roughly the same volume of needs as male
clients, while contacting PA211 slightly more often. This outcome must be treated with caution; the
literature review indicated that gendered differences in behavior that may skew outcomes, such as a
propensity among men to avoid assistance (C. Ross Hatton et al., 2024), which was unaccounted for in this
analysis. Racial differences are minor, save for a disproportionately high volume of unmet needs among
those who did not record their race. This relationship should be a priority target for further instrumentation
and analysis. Lastly, analyzing outcomes via RUCA codes that divide areas into rural, urban, and suburban
proved insufficient. County-level analysis is a far more meaningful geographic metric, but only in isolated
cases, and further development of county-level data procurement (particularly, expanded sample sizes) may
allow for more precise geographical measurement.
Data insights:
•

Age is the most strongly predictive demographic factor, with middle-aged and elderly clients
seeking more assistance overall, as well as a wider variety of assistance types.

•

Race provides mixed results, with minor differences in volume or variety of assistance.

•

Those who chose not to record their race when interacting with PA211 exhibit
disproportionately high volumes of unmet needs. This requires further examination and may
be remedied by further data collection and investigation.

•

Gender is a minor factor, though this conclusion must be treated with caution. Accounting for
social differences between each gender could plausibly change the outcome of future analysis.

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•

First-time clients of PA211, in comparison to repeat clients, have a strong proclivity for
seeking utility assistance. This is the most prominent difference between the two groups.
Conversely, repeat clients tend to seek housing assistance more often, though this effect is
slightly weaker. This finding reflects the longer-term nature of housing expenses, and may
indicate insufficiency of housing assistance services in Pennsylvania.

Suggested interventions:
•

Surveys to determine current assets of clients, as well as their housing and employment status
could improve future datasets. Furthermore, partnerships with both public and private
assistance organizations can help track how much of a client’s income is provided by these
services, bolstering future analysis, especially with regard to repeat versus first-time client
outcomes.

•

Urban, rural, and suburban geographic categories are too broad to grant meaningful results.
County level examinations allowed for actionable results, but only in a few cases. Further
investigation of county-level differences, particularly expansion of sample sizes, may provide
superior geographic data metrics.

Introduction
PA211, my research partner for this project, is an organization that connects Pennsylvania residents
with a variety of services offered by other assistance organizations. Residents seeking assistance are referred

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to as clients. Clients may contact PA211 by dialing 211, texting their zip-code to 898-211, or by calling the
organization directly. The largest of its kind in the state, the organization attempts to direct its clients to
food, housing, healthcare, and many other critical types of assistance. PA211 maintains datasets, provided to
me for this research, which categorizes and tracks the quantities of these factors across all client interactions,
elaborated further in the data section of this paper.
Unmet needs1 are defined as any type of assistance a client was unable to find, even after contacting
PA211. Specifically, it indicates that no service that could be reached by the client could help them with that
need. This paper surrounds five questions posed by the organization which focus on the relationships
between demographic, location, timing factors, and unmet needs. An examination of these relationships may
reveal–and offer solutions for–flaws in PA211’s organizational strategies, data procurement, and
prioritization of vulnerable demographics.
First, PA211 asked what, if any, relationships exist between age and unmet needs. Given the nature
of aging, it is reasonable to infer that stark differences between sought needs exist between age groups. An
examination of each, as well as the composition of their unmet needs, may allow for more precise targeting
of assistance.
Next, I was asked to identify the co-occurrence of categories of unmet needs. Services provided by
partners of PA211 vary greatly, with a range of 17 categories of assistance, all containing numerous
individual services. Awareness of these relationships would allow for improvements in client awareness, e.g.
recommending additional services, should a client pursue assistance with needs that are correlated with
others.

Third, I was tasked with finding relationships between geographic location and unmet needs. PA211
provided client data in conjunction with RUCA codes (Rural-Urban Commuting Area Codes), a land-

1

Italicized text refers to variables from the data used in modelling

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classification system administered by the U.S. Department of Agriculture, grouping individuals into urban,
rural, or suburban. Evidence indicates that these classifications do not fully account for food insecurity once
household characteristics are accounted for (Mabli et al., 2010), prompting an additional investigation into
county-level differences for a later question. Total unmet needs were then compared across the three groups,
followed by multiple regression modelling to uncover whether location still mattered after controlling for
basic demographic factors.
The fourth question asked me to compare the diversity of unmet needs sought by repeat clients and
first-time clients. Behaviors of repeat clients may betray underlying differences in priorities, allowing PA211
to more accurately predict whether clients are going to return, or to determine which avenues of assistance
are insufficient for preventing dependency on assistance services. The literature review has already
demonstrated the superiority of targeting clients differently, rather than using the same approach for all
(Ascarza et al., 2017), lending credence to the development of this style of tracking. For this analysis, three
logistic regression models were built to examine which categories of unmet needs each group tended to seek
assistance with.
Fifth, I was asked to determine relationships between demographic information, location, interaction
time, and unmet needs. This analysis summarized, as well as strengthened, conclusions derived from
previous investigations. Using a multiple regression model, I analyzed which unmet needs clients sought
assistance with depending on when and where they contacted PA211. This analysis was performed using
county-level location information, rather than the provided RUCA codes. This was both to investigate the
potential of county-level geographic measurement as a meaningful metric, and to see if this metric could
more precisely determine these relationships.
Lastly, this paper presents several recommendations that could assist PA211 in more accurately
tracking outcomes, improving data procurement and analysis strategies, and consequently improving

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services. These suggestions include but are not limited to the collection of client outcomes in a more
rigorous manner, such as a phone application or scheduled emails, collection of client asset information, as
well as employment and housing statuses.
Data
Data used in this analysis were sourced from PA211, categorizing the services they
provide referrals for into taxonomy groups, e.g. utilities referring to assistance with gas bills,
electric bills, etc. Also included were demographic factors, such as race, gender, geographic
location (urban, rural, suburban), age, and number of adults in household, alongside factors
such as number of unmet needs, interaction time, and interaction method. To precisely answer
PA211’s questions, tailored statistical analysis methods were applied to each, accounting for any
factors that may have skewed outcomes.
This data uses a three-part structure. First, client is defined as a Pennsylvanian resident
who contacts PA211 with the goal of finding services, whether this be in-person, or by phone.
This contact is referred to as an interaction, and is paired with later follow-up data, indicating
whether a client was able to attain the assistance they sought. Each client is assigned a unique
client ID that corresponds to them as soon as they seek PA211’s services, allowing for
identification of each individual across all interactions. This client ID is paired with client
information, allowing for tracking of demographic information among all clients. Second, each
interaction generates a referral ID, another unique code that corresponds to needs they seek a
referral for, as well as their location, the time of the referral, and whether they. Next, the specific
needs that clients seek are categorized under taxonomy groups, or subcategories that contain each
kind of need, e.g. the food group containing emergency food, food storage, meals, etc. Lastly,

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each of these needs are assigned a number per-interaction, allowing me to count the total number
of times they are sought by a client.
Each interaction was first joined together across each dataset using client ID or referral
ID, specific strings of numbers generated for each client interaction, ensuring consistency when
counting variables. Functionally, client ID allows isolation of clients, each representing a single
point of data, while referral ID isolates which service(s) clients sought from PA211. Using this
system of measurement allowed for precise counting of each client’s interaction, as well as
quantities of each type of service provided by each. Missing or corrupted entries were removed
for all questions.
For the analysis of the relationship between age and unmet needs, the datasets were
joined using referral ID, isolating the specific types and quantities of requested assistance. Only
1,279 age entries were missing out of 17,651, representing roughly 7% of total entries. Age
values of these clients were then binned in intervals <18, 18-25, 26-40, 41-60, and 61+.
Remaining entries were then counted and presented categorically via the stacked bar chart.
Next, when determining relationships between co-occurring unmet needs, datasets were
once again joined together using Referral ID. Because this analysis relied on taxonomy groups,
categories represented by words, it was necessary to convert these categories into numbers to
meaningfully measure them. To ameliorate this, modelling used “One-hot encoding”, a technique
that assigns each need a one or a zero for every interaction, with one indicating the presence of
that need, and zero indicating its absence. For example, if a client sought rent assistance but not
food assistance, the former receives a one, while the latter receives a zero.
To analyze the relationship between environment and unmet needs, interactions were joined
together using client ID. This form of ID was used to measure the prevalence of each type of location

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present in every interaction, specifically whether each client resided in an urban, rural, or suburban
area, and how many unmet needs appeared in each. This analysis used RUCA (Rural-urban
commuting area) codes, a system of classifying each area in the United States based on the volume
and density of commuter activity. A RUCA designation can range from one to ten, with one
corresponding to the most metropolitan area and ten to the most rural, with varying degrees between.
For this analysis, one to three indicates an urban area, four to six indicates a suburban area, and seven
to ten indicates a rural area. Certain RUCA codes, representing empty land or bodies of water, were
removed from this dataset, as well as 259 missing age values.
Furthermore, I examined the diversity of different needs requested by repeat clients,
compared to first-time clients. The modelling controlled for demographic factors (gender, race,
veteran status, and income), allowing for isolation of the effects of repeat client status on
composition of unmet needs. A small number (roughly 800 of 18,519, or 4.3%) of missing entries
were removed from the data.
Finally, I determined the relationships between demographic information, location,
interaction time, and unmet needs. The method of analysis for this question used demographic
factors (race, gender, age), as well as the weekday, hour, and county where each interaction took
place. As before, missing entries were removed, with a slightly higher (1,400) entries dropped in
the age and gender categories. Rather than using RUCA codes to determine location, this analysis
used Pennsylvania counties, allowing for a separate examination of the effects of this
geographical classification method.
Method
To visualize the relationship between age and unmet needs, a simple stacked bar chart was used for
comparison of age intervals. These intervals are displayed on the x-axis, while the y-axis displays the type

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and quantity of unmet needs. This visualization was chosen to clearly demonstrate how needs vary across
age groups, as well as their composition.
To determine relationships between co-occurring needs, a correlation matrix was used - this method
quantifies how often different unmet need categories appeared together during client interactions. Each cell
represents the strength and direction (whether they tend to appear together or not) of the relationship
between each combination of unmet need. To evaluate these categories, one-hot encoded indicators were
used, converting each category into either one (present) or zero (not present). Each interaction was treated as
a separate observation and aggregated per-interaction. Pearson (phi) correlation coefficients were computed
between all need categories, allowing me to quantify each in a range between zero and one, with results
closer to one indicating more co-occurrence, and results closer to 0 indicating less co-occurrence. A filtered
upper-triangle matrix is reported, preventing variables which may correlate together from skewing the
outcome.
My method for determining relationships between geographic location and unmet needs began with
creating a multiple regression statistical model, using several independent variables (urban, rural, suburban,
and age) to predict or explain the number of unmet needs. 𝛽𝛽0 represents the average unmet needs for a

resident of the suburbs, the baseline group. 𝛽𝛽1 represents the difference in unmet needs between urban and
suburban clients, while 𝛽𝛽2 represents the difference in unmet needs between suburban and rural clients.
𝛽𝛽3represents the increase in unmet needs that occurs when a person becomes one year older.
Unmet needs𝑖𝑖= 𝛽𝛽0 + 𝛽𝛽1(𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈)𝑖𝑖 + 𝛽𝛽2(Rural)𝑖𝑖 + 𝛽𝛽3(Age) 𝑖𝑖 + 𝜖𝜖𝑖𝑖
For the question on the unmet needs of repeat clients, three separate logistic regression models were
constructed to understand whether repeat clients of PA211 have differing unmet needs when compared to

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first-time clients. For each model, only the dependent variable was swapped out – this variable being the
particular category of unmet need (food, housing, or utility), allowing me to see whether repeat and first-time
clients have differing probabilities of having those specific kinds of unmet needs. The independent variable,
repeat client (repeat client=1, first-time client=0), indicates whether the client contacted PA211 more than
once, allowing for isolation of the effects of this status on each of the three types of need. These models also
include demographic variables such as gender, race, veteran status, and income, so that the effect of client
status is isolated from the effects of demographic factors.
The results of these models will provide the effect of each status (Repeat Client/First-time Client) on
the overall volume of unmet needs, as well as the likelihood of each for seeking different unmet needs. β0
represents the baseline probability that a first-time client requests the dependent variable, the specific unmet
need in each equation. β1 represents the differing probability of that unmet need being requested by a repeat
client, in comparison to a first-time client. β₂–β₅ are control variables, accounting for gender (male=1, nonmale=0), race (nonwhite=1, white=0), veteran status (veteran=1, non-veteran=0), income (in dollars) and
their reference categories.
logit(P(FoodNeed=1)) = 𝛽𝛽0 - 𝛽𝛽1(RepeatClient) - 𝛽𝛽2(Male) + 𝛽𝛽3(Nonwhite) + 𝛽𝛽4(Veteran) –
𝛽𝛽5(Income)

logit(P(HousingNeed=1)) = 𝛽𝛽0 + 𝛽𝛽1(RepeatClient) + 𝛽𝛽2(Male) + 𝛽𝛽3(Nonwhite) – 𝛽𝛽4(Veteran) –
𝛽𝛽5(Income)

logit(P(UtilityNeed=1)) = 𝛽𝛽0 - 𝛽𝛽1(RepeatClient) – 𝛽𝛽2(Male) - 𝛽𝛽3(Nonwhite) + 𝛽𝛽4(Veteran) +
𝛽𝛽5(Income)

Finally, to uncover relationships between demographic information, location, interaction time, and
unmet needs, a multiple regression model was built, examining the effects of these variables on the

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dependent variable, total number of unmet needs. In this model, β₀ represents the expected number of needs
for the baseline group (the reference categories for race, gender, and location). β₁ captures the change in the
expected number of unmet needs for each additional year of age. β₂ represents the difference in unmet needs
for each gender, and β₃ shows how location (urban, rural, and suburban) affects the number of needs
compared to the baseline location group. β₄ indicates how times of contact, such as the hour or day,
influences the number of unmet needs, given that times of contact may reflect higher urgency or systemic
barriers (e.g. work schedule, childcare, etc.).
To evaluate the predictive performance of the model, R²adj was used. R²adj, or adjusted R2, is a
metric that measures how much demographic and timing factors explain the variation in unmet needs, as
opposed to unknown variables not included in the model. Adjusted R² refines this measure by accounting for
the number of predictors included, making it more reliable when comparing models. Higher values of R² and
adjusted R² indicate a better-fitting model with stronger explanatory power.

Unmet needs𝑖𝑖 = 𝛽𝛽0 + 𝛽𝛽1(Age)𝑖𝑖 + 𝛽𝛽2(Gender)𝑖𝑖 + 𝛽𝛽3(County)𝑖𝑖 + 𝛽𝛽4(Time)𝑖𝑖 + 𝜖𝜖𝑖𝑖
Results
(1): The Relationship between age and unmet needs
The results of the analysis between age and need are visualized via a stacked bar chart. The x-axis
displays age intervals spanning from <18 to >60; the y-axis displays the number of unmet needs,
composition of unmet needs, and the total number of clients in each age group. To the right of the chart is a
key for taxonomy groups, color-coded to better illustrate results.

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Under 18: Minors demonstrate a high need for utility and information assistance, particularly
struggling with family insecurity. However, they are also the age group least likely to contact
PA211 for assistance. I hypothesize that this is due to minors contacting PA211 with the
assistance of an adult. Their needs tend to reflect household challenges, rather than individual
needs.
18-25: Total unmet needs spike for the 18-25 age group in comparison to clients with age <18.
Comparatively, they tend to seek housing, food, and income assistance, while facing legal,
consumer, and public safety concerns. I hypothesize that young adults face instability as they
enter the work force, find housing, or pay rent, and that they struggle with safety concerns.
26-40: Volume of calls increases even more remarkably in this age group, showcasing a
disproportionate number of unmet housing, utilities, financial, childcare, and family service
needs. This age group has the most diverse assortment of needs; I hypothesize that this is the
result of newfound challenges related to managing a household, raising children, and maintaining
employment.
41-60: The 41-60 age group represents the highest overall volume of calls, with housing, utility,
food, and financial assistance remaining at high levels. There is a pronounced increase in unmet
transportation and healthcare needs. I hypothesize that middle-aged clients naturally face
heightened mobility and medical issues, and that these challenges also motivate clients within the
age group to seek mental health service.
61+: In this group, a significant drop in call volumes is observed. Clients above the age of 61 are
most likely to seek assistance in housing, utilities, transportation, and healthcare. This indicates
that as clients age, their needs become even more centered around health and mobility. The

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stacked bar chart shows that, comparatively, elderly clients rely more on medical support
services and transportation than younger age groups.
Interpretation:
The result of this analysis illustrates the comparative differences in unmet needs among age groups.
Younger clients (<18-25) tend to struggle with economic and housing instability, while middle-aged clients
(26-40) have disproportionate unmet needs in childcare and housing assistance. Older clients (41-61+) have
a greater need for transportation and healthcare services, showcasing heightened reliance on community
support to perform daily activities. The results of this analysis, visualized in a stacked bar chart, are
presented in Figure 1.

Figure 1: Composition of unmet needs by age interval

(2): The relationships between co-occurring unmet needs

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The relationships between co-occurring unmet needs are visualized via a correlation matrix, in which
the rate of co-occurrence between two unmet needs is shown in cells where the x and y-axes intersect. The
rate of co-occurrence is expressed as a decimal, on a scale between -1 and one. The former represents
complete lack of co-occurrence, while the latter represents complete co-occurrence. On the correlation
matrix, red indicates a more negative correlation (the 10th percentile), showing that those two needs rarely
co-occur, yellow indicates weak or ambiguous correlation (the 50th percentile) between each need, and green
indicates a positive correlation (the 90th percentile), showing a tendency for those unmet needs to occur
together.
Interpretation:
The correlation matrix demonstrates only a few highly correlated unmet needs. Namely, clothing,
personal, and household needs tend to appear alongside education and food needs. Interestingly,
volunteering and donations tend to occur alongside clothing, personal, and household needs – whether the
poor donate more is a contentious topic in previous scholarship, with several conflicting studies published in
the last 15 years (Pittarello et al., 2022; Malike et al., 2023). Furthermore, miscellaneous government and
economic assistance tends to co-occur with legal, consumer, and public safety services. I hypothesize that
the first two co-occurrences reflect broader attitudes on budget prioritization, e.g. food and clothing, being
smaller purchases, perceived as more ‘reasonable’ for seeking assistance with, and education being
perceived as ‘productive’. The latter two interactions require further examination, as they lack significant
consensus in the literature review (Pittarello et al., 2022; Malike et al., 2023). For the correlation matrix of
co-occuring unmet needs, see Table 1.

Table 1: Correlation matrix of co-occuring unmet needs

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(3): The relationships between environment and unmet needs
Age: Age is a statistically significant predictor (p < .001), but the actual effect on unmet needs is
vanishingly small. The positive coefficient indicates that clients request 0.0005 more unmet
needs per year of age, which cannot culminate in anything meaningful over the course of a
lifespan.
Rural: For people living in rural areas, the result lands at the edge of loose statistical significance
(p = .094), meaning other factors are at play for disparities in unmet needs. The coefficient points
toward rural residents seeking 0.0079 more unmet needs compared to those who live in suburbs.
Despite its consistent appearance across the data, and its loose statistical significance, rurality is
not meaningful.
Urban: The coefficient indicates that urban residents seek 0.0029 fewer unmet needs, a finding
that is not statistically significant (p = 0.471). It is reasonable to conclude that the effects of
urban residence are not explanatory for unmet needs.
Interpretation:

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This analysis sought to determine whether age and geographic location (urban, rural, or suburban)
can help explain or predict unmet needs. Statistical significance was present among three of four groups, all
with negligible effects on unmet needs. Furthermore, the model itself was unable to sufficiently explain
these relationships, as the R²adj = 0.002 - demonstrating that variables driving unmet needs are almost
entirely outside of geographic location when categorized via RUCA codes. See Table 2 for geographic
location and age coefficients.
Table 2: Geographic location and age coefficients

Independent Variables

Estimated Coefficient

Intercept

.0166**[.006]

urban

–.0029[.004]

rural

.0079*[.005]

Age

.0005***[.000]

F-test P-value

.00000524

Values in brackets are standard deviations, values in plain text are coefficient values. Asterisks to the right of
coefficient values represent statistical significance levels. Four asterisks indicate extremely strong significance (p <
0.0001), three asterisks indicate very strong significance (p < 0.001), two asterisks indicate strong significance (p <
0.01), and one asterisk indicates baseline significance (p < 0.05).

(4): The unmet needs of repeat clients versus first-time clients
This analysis explores whether repeat clients have differing needs when compared with first-time
clients. To answer this question, the average rate of appearance for each type of unmet need among both
groups was determined. Next, both groups’ needs in terms of food, housing, and utility assistance were
compared by building three logistic regression models, to investigate whether these differences were
meaningful. Included in the models were controls for demographic factors such as gender, race, veteran

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status, and income. These averages indicated that repeat clients are more likely to seek housing assistance,
but less likely to seek utility assistance, with food only negligibly differing.
Food Needs
According to this analysis, there is no clear distinction between either group regarding the likelihood
of seeking assistance with food needs, as repeat clients exhibit only an average marginal effect of -0.54%.
Further, this variable is not statistically significant (p = .405), meaning it does not explain the already
meager difference. This finding supports the notion that food insecurity is an equal opportunity issue,
affecting all individuals (including those who have previously contacted PA211). The results also suggested
slight demographic effects, with non-white clients being 2.39% more likely to seek food assistance (p <
.001), and male clients being 1.3% less likely to (p = .048). These patterns do not change overall
conclusions.
Housing Needs
This analysis determined that repeat clients are more likely than first-time clients to request
assistance with housing-related issues. The repeat client coefficient was found to be, (.3173) and strong
enough statistically (p < .001) to assume that it was not a chance occurrence, with a marginal effect
indicating that this group is 6.32% more likely to seek housing assistance, overall (p < .001). Given the
long-term nature of housing expenses, factors associated with them (e.g. eviction/foreclosure risk,
homelessness, etc.) are typically unable to be resolved with one interaction. Similar to food needs,
demographics also play a role, with males being 3.19% more likely to seek this form of assistance (p =
.001), and nonwhite clients being 2.51% more likely (p = .007).
Utility Needs
Utility assistance (electricity, heating, or water bills) reverses the trend found in housing needs,
representing the starkest difference between the two groups. Repeat clients were 15.01% (p < .001) less
likely to request utility-related help, with the coefficient for repeat clients being negative (-.6920) and highly

19
significant (p < .001). I hypothesize that this is the result of utility assistance differing from housing
assistance, in that utility bills are far smaller expenses with substantially less risk to the client, should they
fail to pay them. Minor demographic effects, including male clients being 3.79% less likely to seek housing
assistance (p < .001), and nonwhite clients being 4.39% less likely to (p < .001).
Interpretation:
The largest disparity between either group is the disproportionate seeking of utility assistance among
first-time clients. Conversely, repeat clients are less likely to seek housing assistance, though the effect is
smaller. There are no major differences between either group in terms of food needs. Across every logit
model, demographic characteristics do effect outcomes, but not at the scale of repeat client status. Given that
all results are statistically significant, I can conclude that repeat client status is a meaningful metric for
tracking the volume and composition of unmet needs. See tables 3 and 3b for total composition of unmet
needs among repeat and first-time callers, and logistic modelling results for probabilities among repeat
clients, first-time clients, and demographic groups, respectively.
Table 3: The total composition of unmet needs, between first-time and repeat clients
Food Need

Housing Need

Utility Need

First-time clients:

11.30%

26%

42.30%

Repeat clients:

12.10%

33.50%

25.90%

Table 3b: Relationships between repeat-client status and unmet needs, logistic modelling
Independent Variable

Food Need

Housing Need

Utility Need

Intercept

-1.8200*** [.064]

-0.6930*** [.047]

-0.6765*** [.041]

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RepeatClient

-0.0515 [.062]

0.3173*** [.044]

-0.6920*** [.044]

Male

-0.1305** [.066]

0.1602** [.046]

-0.1746*** [.045]

Nonwhite

0.2275*** [.064]

0.1261** [.047]

-0.2024*** [.046]

Veteran

0.0412 [.121]

-0.1905** [.092]

0.1091 [.078]

IncomeNum

-0.0001*** [3.27e-05]

-0.0003*** [2.49e-05]

0.0003*** [1.96e-05]

Values in brackets are standard deviations, values in plain text are coefficient values. Asterisks to the right of
coefficient values represent statistical significance levels. Four asterisks indicate extremely strong significance (p <
0.0001), three asterisks indicate very strong significance (p < 0.001), two asterisks indicate strong significance (p <
0.01), and one asterisk indicates baseline significance (p < 0.05).

(5): The relationships between demographics, county, and unmet needs
This analysis sought to explain how individual demographic factors, such as gender, age, location,
etc., as well as timing variables like hour and weekday, effects rates of unmet needs. Using a multiple linear
regression model to predict the total number of needs per-interaction, I was able to isolate the effect of each
demographic factor, while controlling for interaction timing. Furthermore, I controlled for county, rather
than RUCA code regions, to investigate whether this metric would prove superior.
Age is a strong predictor of need volume: As client age increases, the number of needs perinteraction rises significantly, with 0.0573 more needs per-year of age, starting at 0 (p < .001).
Further, age-squared, a metric for determining whether this trend drops off or accelerates after a
certain age, is -0.0007 (p < .001), indicating that elderly clients seek slightly fewer needs for each
year of age past 61. Findings indicate that, overall, middle-aged adults (41-60) exhibit the highest
overall volume of needs. This implies that working age adults, often battling employment
instability, family obligation, and housing costs, are a core high-need demographic. Notably, age
was by far the most effective predictor in the totality of this research.

21
Gender does not significantly predict need volume: When controlling for geographic location
and demographic information, there was no meaningful difference in presented needs perinteraction between genders. Further, the prediction coefficient (.671) is not statistically
significant (p = .45). Though women contact PA211 slightly more often in many Pennsylvania
counties, the diversity and volume of needs do not differ substantially from male clients, and
statistical significance is lacking in all but two counties. Gender-specific outreach remains useful
for targeted assistance avenues, but gender is not a major predictor of significance.
Race and Ethnicity show mixed effects: Differences in need volume do not significantly differ
between clients of different races. Interestingly, clients who do not provide race/ethnicity
information present significantly higher need volumes than those who do. I hypothesize that
this may indicate privacy concerns, high levels of stress among these clients, or both. This
relationship carries significant weight for PA211’s public outreach strategy, as more information
is required to properly explain it - clients who skip demographic fields are among the highest
need groups, and may require deeper case support.
In some cases, county differences reveal uneven resource environments: Examination of
county-level differences derived more meaningful results than broader urban vs. rural analysis,
but was only statistically significant in two counties. Clients from McKean County have 0.83
fewer average needs (p < .001), while Venango County exhibits 0.41 more needs, on average (p =
.01). I believe that these findings reflect a complex assortment of variables that require further
study, such as local politics, culture, or economy. These results indicate that, in certain cases,
RUCA codes may be an inferior geographic metric, and further investigation on why certain
counties exhibited statistical significance could help develop these metrics further.

22
Time contact variables are not significant predictors: When demographic and county
conditions are accounted for, time of contact does not meaningfully affect overall need volume.
Interpretation:
Overall, age was identified as the strongest predictor, with clients in the 41-60 age group exhibiting
the highest overall volume of unmet needs. Clients who do not report their race are a similarly high-volume
group, suggesting that missing demographic information may signal deeper problems that require further
examination. Geographic differences are evident as well, but only when categorizing via county, and in
certain counties. In contrast, gender, hour, and weekday do not meaningfully predict for overall need
volume. Thus, I find that age, county, and missing ethnic information groups are the worthiest groups for
further examination. See Table 4 for the coefficients, standard-errors, and totals of each variable.
Table 4: Relationships between demographics, timing, county, and needs, multiple linear
regression modelling
Independent variable
Intercept
Gender: Male
Gender:
Female(baseline)
Race: Missing
Race: Other
Race: White
Location: Cameron
Location: Clarion
Location: Clearfield
Location: Crawford
Location: Elk
Location: Erie
Location: Forest
Location: Jefferson
Location: McKean
Location: Potter

Outcome
2.4774****
0.0671
0

Standard error
(0.373)
(0.089)
0

Totals

2.0020**
-0.0656
0.1393
-1.2645
-0.3479
0.0734
-0.2836
-0.5048
0.1367
0.0760
-0.0790
-0.8319****
-0.6451

(0.693)
(0.145)
(0.121)
(0.690)
(0.2300
(0.187)
(0.177)
(0.316)
(0.115)
(0.667)
(0.269)
(0.239)
(0.455)

2384
1665
10341
14
150
253
294
68
1682
16
121
134
106

4415
10478

23
Location: Venango
Location: Warren
Age
Age^2

0.4143**
0.0266
0.0573****
-0.0007****

(0.161)
(0.3202)
(0.014)
(0.0001)

Hour

-0.0119

(0.0110

Weekday

0.0362

(0.025)

359
95

Asterisks to the right of coefficient values represent statistical significance levels. Four asterisks indicate extremely
strong significance (p < 0.0001), three asterisks indicate very strong significance (p < 0.001), two asterisks indicate
strong significance (p < 0.01), and one asterisk indicates baseline significance (p < 0.05).

Conclusion
This paper grants PA211 a preliminary framework for targeted assistance and outreach, as well as
lines of questioning that may derive further meaningful results. Though race, gender, and other demographic
categories display varying levels of effect on unmet needs, age is by far the most notable demographic
predictor of composition, as well as overall volume. Furthermore, this analysis demonstrated stark
differences in the needs of repeat and first-time clients, revealing a propensity to seek housing assistance
among repeat clients, and more strongly, a propensity to seek utility assistance among first-time clients.
Lastly, my research discovered major meaningful differences between two individual counties, while
broader rural and urban classifications failed to derive meaningful results. This suggests that PA211 would
benefit from targeted strategies aimed at assisting middle-aged and elderly clients, better accounting for
differences between repeat and first-time clients, and targeted development of services and data procurement
in underserved counties.

24

References

Ascarza, E., Neslin, S. A., Netzer, O., Anderson, Z., Fader, P. S., Gupta, S., Hardie, B. G. S., Lemmens, A.,
Libai, B., Neal, D., Provost, F., & Schrift, R. (2017). In Pursuit of Enhanced Customer Retention
Management: Review, Key Issues, and Future Directions. Customer Needs and Solutions, 5(1-2),
65–81. https://doi.org/10.1007/s40547-017-0080-0
Brülhart, M., Klotzbücher, V., & Lalive, R. (2023). Young people’s mental and social distress in times of
international crisis: evidence from helpline calls, 2019–2022. Scientific Reports, 13(1).
https://doi.org/10.1038/s41598-023-39064-y
C. Ross Hatton, Bresnahan, C., Anna Claire Tucker, Johnson, J., John, S., & Wolfson, J. (2024). Food for
Thought: the Intersection between SNAP Stigma, Food Insecurity, and Gender. Social Science &
Medicine, 361, 117367–117367. https://doi.org/10.1016/j.socscimed.2024.117367

25
Mabli, J. (2014, March). SNAP Participation and Urban and Rural Food Security . Https://FnsProd.azureedge.us/; United States Department of Agriculture. https://fnsprod.azureedge.us/sites/default/files/SNAPFS_UrbanRural.pdf
Malika, M., Ghoshal, T., Mathur, P., & Durairaj Maheswaran. (2023). Does scarcity increase or decrease
donation behaviors? An investigation considering resource-specific scarcity and individual personthing orientation. Journal of the Academy of Marketing Science, 52. https://doi.org/10.1007/s11747023-00938-2
Pittarello, A., Motsenok, M., Dickert, S., & Ritov, I. (2022). When the poor give more than the
rich: The role of resource evaluability on relative giving. Journal of Behavioral Decision Making.
https://doi.org/10.1002/bdm.2293
Appendix

Table 1: Variable explainer
Variable

Age

Rural

Urban

Male

Name

Non-

Veteran

Transgender

White

Observations

13826

13826

13826

13826

13826

13826

13826

Mean

46.17*

0.23*

0.50*

0.29*

0.28*

0.07*

0.01*

Standard

15.58

0.42

0.50

0.45

0.45

0.26

0.08

Minimum

0

0

0

0

0

0

0

Maximum

96

1

96

1

1

1

1

deviation

*These values represent how often each observation appears in the dataset, since they are expressed as binary
variables. The closer each mean is to 1, the more likely each category is to appear among all observations. Age values
are not expressed as a binary variable, and reflect the actual age (in years) of observations.

26
Table 2: Demographic totals
Gender

Male

Female

Transgender

Did not answer

Other

NaN

4415

10478

30

1267

57

2272

White

Black

Hispanic/Latino

Did not answer

Other

NaN

10341

2324

499

1306

1665

2384

Veteran

Not a veteran

Unavailable

Other

NaN

759

14927

901

448

1484

Urban

Rural

1860

1062

Race

Veteran

Location

Table 3: Composition of taxonomy groups, services sought among all referrals

Utility Assistance

6472

Housing

5117

Food/Meals

2025

Income Support/Assistance

1266

Clothing/Personal/Household Needs

593

Individual, Family, and Community
Support

460

Legal, Consumer, and Public Safety
Services

366

Transportation

329

Health Care

319

Mental Health/Substance Use Disorders

255

Employment

118

27
Education

111

Information Services

72

Volunteers/Donations

67

Other Government/Economic Services

34

Disaster Services

34

Arts, Culture, and Recreation

20

NaN

861