Older Adult Homelessness

The economics of Housing & Homelessness

California Estimates

  • 90% of Older Californians experiencing homelessness were last stably housed in California
  • 40% had their first episode of homelessness after the age of 40
  • 80% were incarcerated at some point in their lives
  • 80% experienced a mental health issue

Source

Source

Source

Source

Overview

Reasons for Poverty are Varied

  • Disability
  • Debt
  • Single Parent
  • Addiction
  • Criminal Record
  • Undocumented Worker

Focus on working age individuals who are willing and able to work but face significant barriers to self-sufficiency

Program of Interest

Social Service Delivery Model

Lift Families out of Poverty

(Individualized & Holistic)

Results

  • Full-time employment increased by 25%

Program costs $22,950

  • Improved self-reported health

Implementation

Pauda

Context

  • Implemented by Catholic Charities Fort Worth 
  • Targets individuals who have the potential to be self-sufficient

(Isn't designed to serve individuals with mental health, substance abuse or other disabilities)

  • Out-of-poverty benchmarks

(a) Living Wage, (b) Reduced participation in transfer programs (c) decreasing debt (d) saving goals

  • Case Management with small case loads

Program

  • Flexible financial assitance

"The case management teams frequently use the financial assistance component as a way of incentivizing behavior."

*I made the word cloud

Initial Assessment takes place over 5-7 in person meetings 

Geography:  

Design

Usual Care

Full Padua Program

Treatment Arms

Enrolled 427 people (2015/16)

Short-term, modest assistance

Design

Why do people decline at the end?

Attrition

Meeting Frequency

Meeting Types

Cumulative Cash

Cash Expenditure

Heterogeneous Effects

Stably Housed

Employed

Yes

No

Yes

No

Data

  • Follow Up Surveys (1-2years)
  • Administrative Data
  • Earnings
  • Government Program Participation
  • Credit Usage

Analysis

y_{ij} = \beta _0 + T_i \beta_1 + x_i \beta_2 + y_{i0} \beta_3 + \varepsilon_i

Linear Model

Nonparametric Residualized Form

y_{ij} = \beta_1 (T_i - \mathbb{E}[T_i \vert x_i, y_{i0}]) + \eta_i

Controls

Age, Race, Gender, Educational Attainment, Marital Status, Employment Status, Earnings, Household Size, cohort, interview month, number of months between baseline interview

Uses post double selection lasso to fit the linear model

Outcomes