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DR. STEVEN GOLIGHTLY DIRECTOR
LOS ANGLES COUNTY CHILD SUPPORT SERVICES
PREDICTIVE ANALYTICS
- at the heart of it!
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LA County is 24% of the State’s child support caseload. Over 1500 employees
Caseload sizes averages 800 per case caseworker
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Analytics is the application of computer technology,
operational research, and statistics to solve problems in business and industry.
For Los Angeles:
• Program Support Division– Programmers and Analysts who research and create statistical reports for management review. Includes a training unit that puts into
practice decisions made by management and then evaluates the effect that policy or training has had on the department
• CSTATs Meeting - Attended by all senior management where departmental performance is reviewed, discussed, and decisions made targeting areas for emphasis or improvement
• Data Sharing - Down to the line level, data is provided for each worker to research and improve their own caseload, performance, and information sharing of best practices.
• SPSS - Software program used for analysis and statistics
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Encompasses a variety of techniques that analyze current and historical facts to make predictions about future, or otherwise unknown, events.
• Statistics
• Modeling
• Data mining
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• Procurement of software • Staffing concerns • Training on softwareModel Development
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• LA County CSSD chose IBM’s SPSS Modeler Professional as our predictive analytic tool since we are already working with SPSS Statistics.
• Client license vs. Desktop license
o Modeler could be used by a number of users from
their own computers.
• Cost
o Purchased one license, which will require users to
schedule when the software can be used.
LA County SPSS Procurement Process
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• Staffing Concerns
o Skill set needed for analytics
o Internal staff (building up skills in analytics)
o Hiring new staff with this kind of background
o Unions
• Training of Software
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Predictive Analytics Goal: By June 30, 2014, develop an initial analytical model which can be used to predict the probability that payments will be received in a child support case. Score all active enforcement cases based on those predictions and distribute them in a format that will allow Child
Support officers to prioritize their work.
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Program Efforts
• Child Support STATS (CSTATS)
• Case Segmentation
• Survival Study
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Research Project Goal
• How important is the receipt of child support to
formerly assisted families in remaining self-sufficient and not reliant upon public assistance?
• Does regular payments or the amount of child support
paid affect a formerly assisted custodial parent’s ability to remain off of CalWORKs?
• Establish an initial baseline data set for future studies
to build upon, both on this topic and new topics, by highlighting questions and data elements that may arise.
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Given all the independent variables previously discussed, the most common welfare recipient would be:
• CP Role Mother (75%)
• Gender Female (90%)
• Ethnicity Hispanic (38%), White (21%), black (9%)
• Primary Language English (79%, includes English as second language)
• CPs Average Age 36 years old
• NCPs Average Age 37 years old
• Number of Children 1-2
• Children’s average Age 11 years old
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Survival Results
Interval Start Time Number Entering Interval Number Withdrawi ng during Interval Number Exposed to Risk Number of Terminal Events Proportion Terminatin g Proportion Surviving Cumulative Proportion Surviving at End of Interval Probability Density Std. Error of Probability Density Hazard Rate Std. Error of Hazard Rate 0 22515 0 22515.000 0 .00 1.00 1.00 .000 .000 .00 .00 1 22515 0 22515.000 12327 .55 .45 .45 .548 .003 .75 .01 2 10188 0 10188.000 5569 .55 .45 .21 .247 .003 .75 .01 3 4619 0 4619.000 1256 .27 .73 .15 .056 .002 .31 .01 4 3363 0 3363.000 583 .17 .83 .12 .026 .001 .19 .01 5 2780 0 2780.000 450 .16 .84 .10 .020 .001 .18 .01 6 2330 0 2330.000 248 .11 .89 .09 .011 .001 .11 .01 7 2082 0 2082.000 239 .11 .89 .08 .011 .001 .12 .01 8 1843 0 1843.000 153 .08 .92 .08 .007 .001 .09 .01 9 1690 0 1690.000 106 .06 .94 .07 .005 .000 .06 .01 10 1584 0 1584.000 98 .06 .94 .07 .004 .000 .06 .01 11 1486 0 1486.000 46 .03 .97 .06 .002 .000 .03 .00 12 1440 0 1440.000 58 .04 .96 .06 .003 .000 .04 .01 13 1382 0 1382.000 39 .03 .97 .06 .002 .000 .03 .00 14 1343 0 1343.000 44 .03 .97 .06 .002 .000 .03 .01 15 1299 0 1299.000 28 .02 .98 .06 .001 .000 .02 .00 16 1271 0 1271.000 20 .02 .98 .06 .001 .000 .02 .00 17 1251 0 1251.000 40 .03 .97 .05 .002 .000 .03 .01 18 1211 0 1211.000 38 .03 .97 .05 .002 .000 .03 .01 19 1173 0 1173.000 31 .03 .97 .05 .001 .000 .03 .00 20 1142 0 1142.000 28 .02 .98 .05 .001 .000 .02 .00 21 1114 0 1114.000 32 .03 .97 .05 .001 .000 .03 .01 22 1082 0 1082.000 18 .02 .98 .05 .001 .000 .02 .00 23 1064 0 1064.000 22 .02 .98 .05 .001 .000 .02 .00 24 1042 0 1042.000 0 .00 1.00 .05 .000 .000 .00 .00 25 1042 0 1042.000 1042 1.00 .00 .00 .000 .000 .00 .00- at the heart of it!
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Challenges
• Staffing
• Civil Service Rules
• Unions
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