Sanction Implementation in the Florida WT Program

1
Sanction Implementation in the Florida WT Program

• Richard Fording
• University of Kentucky
• Sanford Schram
• Bryn Mawr College
• Joe Soss
• University of Wisconsin-Madison

2
Research Questions

• How is the use of sanctions affected by
• Client Characteristics?
• Local Characteristics of Counties/Regions?
• How do sanctions affect
• Client outcomes?
• Regional performance?

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Sanctioning in the WT ProgramA Comprehensive
Analysis

• Project Components
• Causes and Effects statistical analyses of
  administrative data on clients
• Local Practices, Understandings, and Goals
  intensive field research with service providers
  in selected regions
• Individual Knowledge, Beliefs, and
  Decision-Making a statewide electronic survey of
  WT case managers
• Regional Differences in Policy and Approach a
  phone survey of all regional directors.

This presentation is based on administrative
data supplied by Florida DCF. Future
presentations will report findings from other
components of the study.
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Between 2000 and 2004, approximately one-third of
all exits from WT were due to sanctions
(statewide).
5
Based on federal data, Florida appears to close a
relatively high percentage of TANF cases due to
sanctions.
Data Administration for Children and Families,
Sixth Annual Report to Congress, November 2004.
Weaknesses (a) A states placement in this graph
partly reflects the number of cases it closes due
to other reasons (included in the denominator).
(b) Sanctions vary greatly across states. We
advise caution in relying on these percentages
for interstate comparisons.
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Compared to other states, Florida sanctions at a
high rate, even when measured in other ways

• Recommended Technique follow an entering client
  cohort for 18 months calculate the overall
  sanction rate limit comparison to states with
  similar sanction policies.
• This technique has been applied previously to two
  states that have full-family sanction policies
  similar to Florida. Replicating this procedure
  for Florida in the same time period (the November
  2001 cohort), we obtain the following
  percentages
• Illinois 13
• New Jersey 17
• Florida 47
• Floridas sanction rate may be high for a variety
  of reasons, such as
• Fewer exemption categories
• Clients opting out after enrolling and learning
  what is required

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Between 2000 and 2004, most WT sanctions occurred
in the first three months of clients
participation spells
Note These data represent approximately 166,000
sanctions issued between January 2000 and March
2004.
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Sanctions are concentrated early in the spell for
two reasons

• Most Florida TANF spells do not last more than 3
  months.
• The probability of being sanctioned in Florida is
  greatest during the early months of a spell (as
  shown below).

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Research Question How do client characteristics
and local contexts affect the probability that a
client will be sanctioned in the Florida WT
program?

• Individual Characteristics
• Gender Number of Children Race Martial Status
• Work History Age of Youngest Child Age
  Education

Contextual Factors County Unemployment
Rate County poverty rate Country Wage Rate
County racial composition County Caseload Size
County population Local Political Culture
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Statistical Approach An Event History Model of
TANF Sanctions

• We analyze all new adults entering the TANF
  program from January 2001 to December 2002
• 24 separate cohorts based on shared month of
  entry
• We track these adults only through their first
  spell of receiving cash assistance and for a
  period that is no more than 12 months.
• After applying these criteria, our analysis is
  based on approximately 60,000 TANF adults

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What Types of Clients are Most Likely to
Experience a Sanction?

• Clients with Less Education
• Compared to a similar client with more than 12
  years of education
• A client with only 12 years is 12 more likely to
  be sanctioned
• A client with less than 12 years is 43 more
  likely to be sanctioned
• Clients with Weaker Earnings Histories
• Compared to a similar client who earns 4,000 in
  the quarter before program entry
• A client who with no earning in the quarter
  before entry is 6 more likely to be sanctioned
• Younger Clients
• Compared to a similar client who is 30 years old
• A 20 year-old client is 20 more likely to be
  sanctioned

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Which Types of Clients are Most Likely to
Experience a Sanction?

• Male Clients
• Compared to a similar client who is a woman
• A male client is18 more likely to be sanctioned
• Married Clients
• Compared to a similar client who is not married
• A married client is 14 more likely to be
  sanctioned
• Clients with Older Children
• On average, clients with older children are more
  likely to be sanctioned, regardless of how many
  children they have.

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Client Race and EthnicityEffects Vary across
the Participation Spell

• Compared to White clients, Black clients are
• 13 less likely to be sanctioned in Month 1
• Equally likely to be sanctioned in Month 3
• 12 more likely to be sanctioned in Month 6
• Compared to White clients, Hispanic clients are
• 16 less likely to be sanctioned in Month 1
• 9 more likely to be sanctioned in Month 3
• Equally likely to be sanctioned in Month 6

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Sanction rates vary considerably from region to
region and from county to county.Percent of
TANF Adults Sanctioned During First TANF Spell
Based on average of 24 cohorts entering TANF
from January 2001 to December 2002
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Local Differences in Sanctioning Could Arise
from Many Sources

• Caseload characteristics (as just described)
• RWB decisions and LOPs
• Service providers and contracts
• Intake and orientation procedures
• Local cultures and understandings
• Local job availability, public transit, and so on
• And many more
• Our approach focuses on local discretion in the
  sanction process, seeking to identify the
  contextual factors that influence sanction
  decisions as part of the overall case management
  process.

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Key Points of Discretionin Local Sanction
Implementation

• Informing clients about TANF rules
• Assessing clients needs and clients abilities
  to participate in regular work activities
• Monitoring participation in required activities
• Interpreting and applying good cause exceptions
  
• Identifying circumstances beyond the
  participants control
• Initiating sanctions e.g., how and when a
  client is informed of pre-penalty status or an
  impending sanction
• Reengaging clients e.g., how and when a client
  may cure a sanction, and at whose initiation

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How Do Differences in Local Economic Context
Affect Sanction Rates?

• Unemployment
• The local unemployment rate is unrelated to the
  county sanction rate
• Poverty
• All else equal, sanction rates tend to be higher
  in counties with higher poverty rates
• Wages
• The local wage rate (food service/drinking
  places) is unrelated to the county sanction rate

These results are based on an analysis which
controls for county-to-county differences in
clients traits.
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How Do Differences in Local Social Context Affect
Sanction Rates?

• Welfare Usage
• All else equal, sanction rates tend to be lower
  in counties with larger per capita TANF caseloads
• Population Size
• All else equal, sanction rates tend to be higher
  in more highly populated counties
• Racial/Ethnic Context
• All else equal, sanction rates tend to be lower
  in counties where Hispanics make up a larger
  percent of the population
• The size of the local black population is
  unrelated to county sanction rates

These results are based on an analysis which
controls for county-to-county differences in
clients traits.
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What Difference Does It Make?The Consequences of
Sanctioning

• In addition to studying why sanctions are imposed
  in some cases more than others, we are also
  studying how sanctions affect several key
  outcomes
• Return Rates and Future TANF Usage
• Odds of Being Sanctioned in the Future
• Client Earnings after Exit
• Regional Performance

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Sanctions Increase Return Rates
Compared to clients who exit for other reasons,
sanctioned clients are more likely to return to
the TANF program
Note This table is based on 15 cohorts of new
TANF clients, entering TANF from January 2001
through March 2002.
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Sanctions Speed Up the Return Process
Compared to clients who exit for other reasons,
sanctioned clients return to TANF more quickly
Note This table is based on 15 cohorts of new
TANF clients, entering TANF from January 2001
through March 2002.
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Do sanctions reduce the odds that a client will
be sanctioned in the future?

• After sanctioned clients return to TANF, are they
  more or less likely to be sanctioned than other
  clients?
• To the extent that sanctions teach clients to
  comply with program rules, they should make
  clients less likely to be sanctioned in the
  future.
• To the extent that sanctions establish a bad
  reputation, making future case managers less
  likely to give clients the benefit of the doubt,
  they should increase the odds of a future
  sanction

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Compared to clients who were not previously
sanctioned, clients sanctioned in the first spell
are just as likely or more likely to be
sanctioned in the second spell
Note The sample for this analysis consists of
all clients identified in the previous two tables
who returned for a second spell prior to April
2003.
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How Do Sanctions Affect Client Earnings?

• Pre-existing Differences as noted earlier,
  clients with weaker earning histories are more
  likely to wind up being sanctioned. Thus, we
  should expect sanctioned clients to continue to
  have lower earnings after program exit.
• Key Question Do sanctions reduce or widen this
  gap in earnings? That is, do sanctions put this
  low-earning group of clients at a greater
  disadvantage, or do they push them toward the
  higher earnings of non-sanctioned clients?
• Method To answer this question we compare
  quarterly earnings of sanctioned clients to
  non-sanctioned clients, before entering and after
  exiting TANF

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The Earnings Gap Expands
Compared to the earnings gap prior to TANF entry,
sanctioned clients fall even farther behind
non-sanctioned clients after exiting TANF
Note The sample for this analysis consists of
all clients with exactly 12 years of education
entering in January, April, July or October, and
exiting in the third month of their spell (March,
June, September, December), years 2001-2002 (for
a total of 8 cohorts). Results are similar for
clients with less than 12 years of education. The
selection of these entry/exit months and spell
length (3 months) insures that the entire TANF
spell spans exactly one quarter, and the exit
occurs in the last month of the TANF quarter.
During the three quarters prior to entering TANF,
clients were never on TANF. After exiting,
clients may or may not have been on TANF (for
additional spells).
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The Earnings Gap Expands
The Probability of Earning Full-Time Equivalent
Wages in a Quarter Again, compared to the gap
prior to TANF entry, sanctioned clients fall even
farther behind non-sanctioned clients after
exiting TANF
Note This analysis utilizes the same sample as
the previous slide, but includes clients at all
educational levels. An important difference in
this analysis is that it controls for differences
in individual traits of clients across the
sanction and non-sanctioned group (such as race,
education, age, etc.). Rather than comparing
average earnings, we compare the probability that
a client in each group earned 2048 (which we
determined to be roughly equivalent to working 30
hours per week at close to minimum wage).
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How Do Sanctions Affect Regional Performance
Over Time?

• Method of Analysis
• We track the participation rate for each region
  for each month over one fiscal year (July
  2002-June 2003)
• We analyze how monthly participation rates are
  affected by
• the regions sanction rate 0-3 months earlier
• the return of sanctioned clients 0-3 months
  earlier
• Our analysis controls for regional caseload
  characteristics and economic conditions

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Regional Caseload Characteristics and Economic
Conditions Matter for the Participation Rate

• All else equal, a regions participation rate
  goes up as
• The caseload size decreases
• The length of participation spells increases
• The of female clients increases
• The of black clients decreases
• Client earnings prior to TANF increase
• Client education levels increase
• The local unemployment rate increases

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Sanctioning and Regional Performance

• On its own, a regions sanction rate has no
  effect on its participation rate 0-3 months later
• Consistent result across all analyses
• However, when regions sanction more, they
  increase the percent of returning clients who
  were previously sanctioned. These returning
  clients have a significant negative effect on the
  regions participation rate
• A 10-point increase in the percent of returning
  clients who have been sanctioned produces, on
  average, a 2.4-point decline in a regions
  participation rate
• This effect could exist for a variety of reasons
  e.g., sanctioned clients may be less able to
  complete participation requirements

Regardless, the net effect of greater sanctioning
is to diminish performance by reducing the
participation rate
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Summary

• Florida sanctions at a relatively high rate
• Most sanctions occur early in a participation
  spell
• Clients characteristics associated with
  sanctions -lower education
• -lower earnings
• -younger
• -male
• -married
• -having older children
• -whites early in the spell
• -blacks later in the spell

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Summary

• Contextual characteristics associated with higher
  rates of sanctioning
• -Higher Poverty Rates
• -Lower Per Capita Welfare Usage
• -Larger Population
• -Larger Hispanic Population

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Summary

• Sanctions affect client outcomes and regional
  performance.
• Clients who get sanctioned tend to have had lower
  earnings prior to program entry. After being
  sanctioned, their earning fall further behind
  non-sanctioned clients
• In regions that sanction more, sanctioned clients
  return at higher rates. As a result, these
  regions produce lower participation rates in the
  ensuing months

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Future Research

• Continuation of field research to link these
  findings to specific practices and understandings
  in the regions
• Extension of statistical research to more
  contemporary data.
• A statewide survey of case managers to clarify
  how frontline knowledge and decision making may
  contribute to these patterns.
• A phone survey of regional directors to clarify
  how regional policies and strategies contribute
  to these patterns