From Data to Insight: Using
Support Vector Machines to
Identify Key Poverty
Determinants
Anatoli Nachev
Business Information Systems, University of Galway, Galway, Ireland
anatoli.nachev@universityofgalway.ie
ABSTRACT
 This is a case study examining cross-sectional microdata obtained
from the IPUMS database to assess the factors that influence
poverty in the US population.
 Methods: using ML predictive models based on support vector
machines.
 Objective: estimating importance of factors affecting poverty:
 predisposing
 socio-demographic
 socio-economic
 Analysis:
 sensitivity of the variables
 variable effect characteristics (VEC) analysis
Support Vector
Machines
Supervised ML method : binary
classification.
Finds an optimal separating
hyperplane between classes.
For non-separable data, SVM uses the
kernel trick
Kernel function Φ(x) maps Input space
into a higher dimensional.
Linear, polynomial, Gaussian (RBF),
sigmoid (htan), Laplace, …
SVMs are robust, versatile, and widely
used ML technique.
DATA
 IPUMS USA cross-sectional data for 2021-22 from 16 federal censuses.
 Originally 87 variables, reduced to 49 to avoid multicollinearity
 Originally 985,790 records, imbalanced dataset with distribution of 90.2% to
9.8% for absence of poverty versus poverty.
 Independent variables grouped into 3 categories:
 Predisposing: 14 variables
 Socio-demographic: 15 variables
 Socio-economic: 20 variables
Correlation Analysis of Variables
Predisposing
Socio-demographic
Socio-economic
Correlation matrix plots for each category. Positive and negative correlations are
represented by colored circles
UHRSWORK
COUPLETYPE
MORTGAGE
EMPSTAT
MORTAMT1
PROPINSR
MARST
EDUC
INCTOT
INCSS
ROOMS
BEDROOMS
ACREHOUS
RENTGRS
INCRETIR
RACE
COSTWATR
VALUEH
AGE
SCHLTYPE
LANGUAGE
NFAMS
COSTELEC
DIFFPHYS
HCOVANY
METRO
DIFFMOB
DIFFREM
HISPAN
MIGRATE1
COSTGAS
REGION
DIFFEYE
DIFFCARE
CITIZEN
DENSITY
DIFFSENS
INCWELFR
FARMPROD
SEX
COSTFUEL
MULTGEN
YRSUSA1
DIFFHEAR
NSUBFAM
FARM
0
0.05
0.1
0.15
0.2
0.25
Chi2: Cramer's V
0.3
0.35
0.4
Chi-Square test for feature significance using Cramer’s V measure
EXPERIMENTS AND ANALYSIS
 Using R.
 Data partitioning.
 20% for test partition.
 80% split into 2 : 1 ratio for training : validation partitions.
 5-fold cross-validation.
 Performance results averaged from 10 instances of each model.
 Performance metrics: accuracy (Acc), sensitivity (TPR), specificity (TPR),
precision, and F1.
 ROC analysis, AUC.
Acc
AUC
TPR
TNR
Precision
F1
Predisposing
Sociodemographic
Socioeconomic
58.9%
0.604
60.4%
57.4%
57.75%
58.9%
70.3%
0.729
69.4%
71.2%
71.4%
70.4%
80.1%
0.872
73.8%
86.9%
86.3%
79.6%
Performance metrics of SVM models trained by three
categories of variables.
0.00
0.05
0.10
Predisposing factors:
0.15
0.20
0.25
0.20
0.25
RACE
HISPAN
DENSITY
REGION
 RACE, HISPAN and DENSITY
dominate over the others with
collective significance score
about 50%.
FARMPROD
DIFFREM
DIFFCARE
DIFFHEAR
DIFFSENS
DIFFEYE
FARM
METRO
DIFFMOB
DIFFPHYS
0.00
0.05
0.10
0.15
Significance of predisposing variables
Socio-demographic factors:
0.00
0.05
0.10
0.20
EMPSTAT
EDUC
NSUBFAM
NFAMS
COUPLETYPE
VALUEH
MIGRATE1
SCHLTYPE
AGE
LANGUAGE
BEDROOMS
ROOMS
ACREHOUS
CITIZEN
YRSUSA1
MULTGEN
SEX
MARST
 EMPSTAT, EDUC, and NSUBFAM are at the top of
importance with collective importance of
about 50% in the entire category
0.00
VEC analysis of EMPSTAT and EDUC.
0.15
0.05
0.10
0.15
Significance of socio-demographic
variables
0.20
Socio-economic factors:
 Two leading factors of poverty in this group
are the total personal income (INCTOT) and
welfare (public assistance) income
(INCWELFR) .
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.25
0.30
INCTOT
INCWELFR
COSTFUEL
COSTELEC
RENTGRS
HCOVANY
PROPINSR
UHRSWORK
COSTWATR
INCSS
COSTGAS
MORTAMT1
INCRETIR
MORTGAGE
0.00
VEC analysis of INCTOT and INCWELFR
0.05
0.10
0.15
0.20
Significance of socio-economic variables
CONCLUSIONS
 Using survey data for training ML models and based on
model performance metrics:
 Socio-economic factors play primary role as they serve as
fundamental determinants of individuals' and communities'
economic well-being.
 Next in importance emerge the socio-demographic factors that
aid in comprehending how characteristics like age, gender,
household composition, ethnicity/race, and geographical
location interact with the socio-economic factors, affecting
poverty for both individuals and communities.
 Least in importance are the pre-disposing factors, such as health
issues, discrimination, and geographic location. They play role in
shaping individuals' susceptibility to poverty and can influence
ability to overcome economic challenges.
Thanks!
Anatoli Nachev
Business Information Systems,
University of Galway,
anatoli.nachev@universityofgalway.ie