Pathway of Hope - The Salvation Army's Solution to Break the Cycle of Poverty

Posted on Feb 6, 2017


“This Administration has declared unconditional war on poverty and I have come here this morning to ask all of you to enlist as volunteers.”

- President Lyndon B. Johnson


Since President Lyndon B. Johnson's declaration of a war on poverty 50 years ago, poverty in the United States remains persistently high. Among the world's 35 wealthiest countries, the United States ranks second highest in childhood poverty. Although the GDP per capita has more than doubled in the last half century, 16 percent of all Americans live at or below the poverty threshold established by the United States government. 

Poverty is not an affliction of the unfortunate few. It is a national epidemic. The 16.1 million young people in poverty today represent 20% of all American children, up from 14.3% in 2009. This is the highest level of childhood poverty since 1993. It is an epidemic that must be stopped.

Importantly, this increase reflects a radical shift in the face of poverty over the last 50 years. In that time, .the United States has experienced a disproportional increase in both single parent households and children who reside in poverty.

For over 150 years, The Salvation Army has sought to combat hunger and meet the need for those in poverty. The Pathway of Hope initiative was introduced to Eastern Territory of The Salvation Army, beginning in 2016, and seeks to be a real solution to help families break out of the perpetual cycle of intergenerational poverty.

Pathway of Hope is targeted and intensive case management to assist families striving to break free from intergenerational poverty. The Salvation Army forms a crucial partnership with families in need. Families participating in the program possess the desire to change their situation, and are willing to share accountability with The Salvation Army for planned actions. Through achieving increased stability, these families find a newfound hope, propelling them forward on their journey to self-sufficiency.

This analysis takes a deeper look at the initiative, running in over 25 local communities within The Salvation Army’s Eastern Territory. The in-take process individually evaluates a family in crisis, and identifies custom and critical goals ranging from securing employment to finding affordable childcare.

Poverty in the U.S. is an epidemic – including one in five children, according to the latest Census figures. Children who live in poverty for half their childhood are 32 times more likely to remain in poverty (according to The Urban Institute). The Salvation Army works with many of these families by addressing immediate needs.

Pathway of Hope is the next step for  to help them break the cycle of poverty.


After a series of conversations with individuals at the Salvation Army's Eastern Territorial Headquarters, I received data sets relating to the Pathway of Hope (P.O.H.) with the intention to analyze the current effectiveness of The Pathway of Hope program.

Unfortunately the data currently collected by the Salvation Army is insufficient to quantify and rigorously answer this very important question, "Is the Pathway Of Hope effective?" Upon further inspection, the data shows two things:

  1. A unique entry for each client (including Entry/Exit dates for the POH initiative, racial demographics, age, gender, Salvation Army Corps where services are rendered, and, an indication if they are the head of the household (one per family), and some subsidized housing categorical information).
  2. A unique entry for each Goal/Need (including Goal Classification, the unique identifier (Client.Uid) corresponding to each individual in the program, and an outcome(completed/in-progress/etc)

The data in this program is gathered from 26 separate locations with differing computer systems, varying structure for inputting client data. Also, in its current state, covering 26 geographies, the Pathway of Hope initiative has been running for only a little more than 1 year. These factors played a major role in the uneven consistency, validity, and integrity of the data collected, but these hurdles were ultimately overcome by adjusting the scope of the analysis.

Rather than attempt to analyze the effectiveness of POH as a whole, I analyzed the distribution of Client Goals, and their relationship to various factors such as race, gender, geography, and household formation.


The data from the Salvation Army came in two separate .csv files, below is a brief overview of the two data sets, Poh (Pathway Of Hope Main data set) and GC (Goal Classification data set)

'data.frame': 665 obs. of 15 variables:
Client.Uid : int 14 15 16 17 18 19 20 20 20 21 ...
Entry.Exit.Entry.Date : Factor w/ 134 levels "1/10/2017","1/11/2016",..: 68 68 68 68 68 74 74 73 73 74 ...
Entry.Exit.Exit.Date : Factor w/ 30 levels "","1/12/2017",..: 14 14 14 14 14 18 18 18 1 18 ...
EE.Provider : Factor w/ 26 levels "POH EPA Carlisle(164)",..: 13 13 13 13 13 11 11 11 11 11 ...
Household.Relate..Head.Of.Household : Factor w/ 3 levels "","No","Yes": 3 2 2 2 2 3 2 2 2 2 ...
Household.Type : Factor w/ 5 levels "","Female Single Parent",..: 5 5 5 5 5 2 2 2 2 2 ...
Entry.Exit.Group.Id : Factor w/ 185 levels "","1,001","1,013",..: 118 118 118 118 118 161 161 62 61 161 ...
Client.Date.of.Birth : Factor w/ 490 levels "","1/1/1971",..: 333 372 431 401 123 19 442 442 442 427 ...
Client.Primary.Race : Factor w/ 9 levels "","American Indian or Alaska Native (HUD)",..: 9 2 2 2 2 4 4 4 4 4 ...
Gender : Factor w/ 3 levels "","Female","Male": 3 2 3 3 3 2 3 3 3 2 ...
Client.Ethnicity : Factor w/ 6 levels "","Client doesn't know (HUD)",..: 6 5 5 5 5 6 6 6 6 6 ... Factor w/ 11 levels "","Data not collected (HUD)",..: 1 1 1 1 1 1 6 6 6 6 ...
Income.from.any.source.142. : Factor w/ 70 levels "","0","1,000",..: 13 1 1 1 1 62 1 1 1 1 ...
Entry.Exit.Destination : Factor w/ 9 levels "","No exit interview completed (HUD)",..: 4 4 4 4 4 5 5 5 1 5 ...
Client.Race : Factor w/ 6 levels "","Black or African American (HUD)",..: 6 5 5 5 5 2 2 2 2 2 ...

Upon deeper inspection of the Poh data it became apparent that the data was filled with blank, incorrect, and duplicate values. Of the initial 665 clients, 102 clients were duplicated in the data set between 2-5 times, and among duplicate entries the Household.Type, Entry/Exit Dates, Race, and Head of Household entries varied dramatically.

This left me with the task of filtering out duplicate entries, and trying to determine which entries were a result of the case worker having difficulties inputting information into the database and duplicating the entry or creating a new entry to be used as a placeholder in the database system.

Due to the limited amount of unique data for the child clients, I focused this analysis on the clients with the attribute Head of the Household = "Yes", which corresponds to analyzing the clients on a per family basis.

The result is a narrow narrow pool of 148 clients in this analysis, hardly sufficient to determine the success of the program, but useful to determine areas of need and perhaps influence the deployment of services by the Salvation Army.256,713,740

'data.frame': 487 obs. of 8 variables:
Client.Uid : int 14 14 14 14 14 14 14 14 14 14 ...
EE.ID : int 58 58 58 58 58 58 58 58 58 58 ...
Goal.ID : int 4 9 14 19 24 39 51 56 57 67 ...
Goal.Classification: Factor w/ 17 levels "Case Notes","Child Care",..: 8 9 5 7 10 7 16 6 13 8 ...
Goal.Type : Factor w/ 89 levels "Access legal aid",..: 18 4 21 72 54 62 43 34 15 18 ...
Goal.Date : Factor w/ 183 levels "1/11/16","1/13/16",..: 96 96 96 96 96 143 143 135 135 138 ...
Goal.Status : Factor w/ 3 levels "Closed","Identified",..: 1 1 1 1 1 1 1 1 1 1 ...
Goal.Outcome : Factor w/ 5 levels "","Abandoned",..: 3 3 3 3 3 3 3 2 2 5 ...

The Goal Classification data set has 487 individual entries, ranging from 1 to 21 goals for 95 unique clients. Fortunately, most of this subset of Pathway of Hope clients are nearly all Heads of Households, so after merging the two data sets(outer join by = "Client,Uid") the end result is a collection all goals for 78 families.

Below is a table of the 25 Salvation Army Corps that function as the main point of contact for the Pathway of Hope clients.

EPA Carlisle EPA Harrisburg EPA Lancaster
EPA Reading MA Boston Central MA Boston South End
MA Chelsea MA Lowell MA Waltham
NEO Akron NEO Canton NEO Clev-West Park
NEO Medina NEO NWOAS – Toledo Temple NNE Concord
NNE Manchester NNE Nashua SNE Bridgeport
SNE Meriden SNE New Haven WPA McKeesport

Below is a table of the 12 Goal Classifications that categorize each of the POH client goals

Child Care Economic Education
Employment Health Household Necessities
Housing Legal Other
Social Development & Relationships Transportation


 Goals by locationCorp Location

EPA Carlisle has by far the most goals compared to any other Corp participating in the Pathway of Hope.

Please note, the top chart depicts the number of family goals grouped by the dropdown menu (in this case, EE.Provider - Location), the bottom chart depicts the same chart as above, but it is additionally filtered by the 11 Goal Classifications, so the impact of each goal can be determined.

Goals By Household Formation


The distribution of goals heavily skews towards single female parent households, followed by two parent households at a distant second.

Goals by Gender 


The vast majority of families have a single female parent household, so it seems natural that the number of female goals is similarly much higher than the number of male goals. Interestingly, when filtering by the Goal Classification "Child Care," the result was only women

Goals by Race


The majority of families fall under the "while" primary race by a large margin, but when filtering by the Goal Classification "Child Care", the Black/African American families have the highest need.

Analysis of Goal Classification 

Goal Classification

The above Rose area chart depicts the difference in quantity of goals between each of the 11 Goal Categories. Unlike a traditional pie chart, each 'wedge' is formed with the same angle, so larger categories form 'longer' wedges rather than smaller categories. Most common Goal Classifications from highest to lowest: Economic, Education, Housing, Health, Employment, Household Necessities, Legal, Other,  and Child Care.


The Pathway of Hope is giving families stuck in the cycle of poverty the opportunity to work their way towards self-sufficiency and stability. For any rigorous analysis of the Pathway of Hope program to be completed, there needs to be a complete overhaul of the data collection procedures and data collection methods. Numerical metrics such as income need to be collected at multiple points in time.

Ideally it would be collected during intake into the program, at regular intervals while participating in the program (perhaps every 6 months), upon completion of the program, and in the following months and years (again perhaps every 6 months). 

As a result of removing duplicate and erroneous data, the resulting pool of data is unfortunately too small to draw conclusions to apply to the entire population of American families in the cycle of poverty. However, I believe that the results of this analysis can be useful in assisting the Salvation Army in fundraising and optimizing the deployment of their assortment of services.  

Upon sharing preliminary results with the Salvation Army, I was informed that my influence contributed to overhauling the data collection methods and policies for the Pathway of Hope initiative in addition to updating their database infrastructure. In the coming months, I plan to continue my partnership with the Salvation Army, to add additional data as it is gathered and hopefully deliver useful analysis to help further the mission of Pathway of Hope.


The skills the author demonstrated here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.


About Author

Scott Edenbaum

Scott Edenbaum is a recent graduate from the NYC Data Science Academy. He was hired by the Academy to assist in buildout of the learning management system and seeks to pursue a career as a Data Scientist. Scott's...
View all posts by Scott Edenbaum >

Related Articles

Leave a Comment

No comments found.

View Posts by Categories

Our Recent Popular Posts

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI