WIOA Performance Target Setting

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1 WIOA Performance Target Setting

2 Overview Simple Overview of WIOA Accountability Measures Expectations WIOA Target Setting Factors Impacting Targets Statistical Modeling Scheduling Role of Data Review of one DOL Statistical Model QUICK ACRONYMS DOL Department of Labor ED Department of Education RSA Rehabilitation Service Administration OCTAE Office of Career, Technical & Adult Education 2

3 Common Accountability WIOA 116 Identifies a set of 6 Performance Indicators to be Used with Titles I-IV Employment Quarter 2 Post-Exit Includes Employment or Enrollment for Title I Youth Employment Quarter 4 Post-Exit Includes Employment or Enrollment for Title I Youth Median Earnings Quarter 2 Post-Exit Adult Credential Rate Not Applicable to Title III Wagner-Peyser Measurable Skills Gains Not Applicable to Title III Wagner-Peyser Effectiveness Serving Employers To Be Defined by DOL & ED Will Be Applied in Integrated Manner Across Titles 3

4 Expectations & Consequences Two Levels of Accountability State expects Performance within 5% of target WIOA has 3 different sets of Performance Expectations Average % of Target for all Measures in a Program >=90% Average % of Target for all Programs on a Measure >=90% % of Target on each single Program-Measure >=50% Greater Consequences for Failure under WIOA 2 nd Consecutive Year of Failure by a State results in a 5% Reduction to the Governor s 15% Statewide Funds 3 rd Consecutive Year of Failure by a local Board results in: Appointment of a New Board Prohibition in the use of certain Providers & Partners found to have achieved a poor level of performance Other Significant Actions as Deemed Appropriate TWC generally expects performance to be within 5% of Target Failure to meet expectations may result in Technical Assistance, a Technical Assistance Plan, or an Intent to Sanction or Sanction (each with Corrective Action Plan) 4

5 WIOA Target Setting Factors to be Considered in STATE Target Setting Characteristics of those Served (Casemix) Economic Conditions National Targets Set by DOL & ED Continuous Improvement Targets of Other States Casemix & Economic Conditions to Be Addressed Thru Statistical Model Statistical Models require LOTS OF DATA to create Historic results under the new Measures Participant Casemix of those included in results Economic Conditions relevant to the results DOL & ED do not have necessary data for all measures 5

6 Historic Data & Model Development DOL has 10+ years of Title I Adult/DW/Youth Data Able to develop Usable Models except for Median Earnings Q2 Post-Exit (Youth) Measurable Skills Gains (Adult, DW, & Youth) Great Statistical Technique but Not Great Models Credential Denominator Changed after Models Developed Historic Data based Exit on ANY Qualifying Service Youth Program different under WIOA than WIA DOL had several years of Title III Wagner-Peyser Data Able to develop Usable models Had to use Q3 data as Proxy for Q4 ED/RSA has years of Title IV VR Exiter data but no outcome data consistent with WIOA ED/OCTAE has NO Title II AEL Exiter data & Limited outcome data consistent with WIOA (MSG only, kinda) 6

7 Initial Implementation DOL & ED designated some measures as Baseline Baseline Measures are reported but not subject to Target Setting / Accountability Effectiveness Serving Employers also listed Baseline Measures Include: Title I Median Earnings Quarter 2 Post-Exit (Youth) Measurable Skills Gains (Adult, DW, Youth) Title II AEL All EXCEPT Measurable Skills Gain Title IV All Measures Effectiveness Serving Employers DOL & ED Proposed Piloting 3 measures 7

8 Where are We Now? DOL Negotiated BCY17/PY16 targets with States Negotiations Primarily based on Statistical Models but States were able to present additional information States Negotiated BCY17 targets with Boards ED/OCTAE Negotiated PY16 targets with States Their targets were not in compliance with statute No Statistical Model No Consideration of Casemix ED/RSA didn t conduct Negotiations All Measures in Baseline Status for PY16 We expect the same for BCY18/PY17 8

9 WIOA Target Setting has 2 Phases Targets initially negotiated using Statistical Models that focus on ASSUMED Casemix & Economic Conditions Targets are later retroactively adjusted Down or UP based on the ACTUAL Casemix & Economic Conditions If actual Casemix or Economic Conditions are better than expected, targets go up at the end of the year Easier to Serve Casemix? Better Economic Conditions? Targets goes Up Year End Adjustments for Current Year occur AFTER Initial Targets are Set for Next Year Initial Negotiations for the new year in May/June Adjustments for the last year in Aug/Sept 9

10 BCY18/PY17 Target Setting Schedule Initial Negotiations May-June 2017 Year End Adjustments August-September 2018 Measure Exiter Period Measurement Period Employment Q2 Post-Exit Employment/Enrollment Q2 Post-Exit Median Earnings Q2 Post-Exit Employment Q4 Post-Exit Employment/Enrollment Q4 Post-Exit Credential Rate Jul 2016 Jun 2017 Or 2016Q3 2017Q2 Jan 2016 Dec 2016 Or 2016Q1 2016Q4 Jan 2017 Dec 2017 Or 2017Q1 2017Q4 Jan 2017 Dec 2017 Or 2017Q1 2017Q4 10

11 The Point? WIOA Performance Accountability is NOT about TARGET SETTING WIOA Performance Accountability is about PERFORMANCE EVALUATION We don t set a future Target & Aim for it Even without the Lag, the negotiated targets end up being changed based on actual Casemix & Economic Conditions The Key to WIOA Performance Accountability is Obtaining, Learning From, and Using DATA What Data is required? What does the Statistical Adjustment Model account for & what does it miss? What factors might exist or be changing that suggest past performance will not be indicative of future performance? 10

12 Data Requirements WIOA Required DOL & ED to Develop Joint Performance Reporting Specs & Reports Participant Individual Record Layout (PIRL) The PIRL contains the basic Elements required to be reported on EVERY Participant 76 Different Elements (though some are conditional) The Departments each ADDED to the Joint PIRL DOL-PIRL adds 100s of elements for DOL programs RSA incorporated the 76 Joint PIRL elements into an updated RSA-911 (which itself has 100s of elements) OCTAE is largely doing their own thing thru their National Reporting System (NRS) 12

13 Joint PIRL Elements PIRL is made up of 4 Main Sections Participant Information Tells who was served Tells the status of the those served Helps the Statistical Model account for it Participation Information Tells which programs contributed to Success (or lack thereof) Identifies the Types of Services provided Employment/Earnings Outcome Information Education Outcome Information 13

14 Participant Information SSN is not reported to DOL/OCTAE but is Critical No SSN? Harder to report outcomes Date of Birth Sex Race Ethnicity Employment & Educational Status at Participation Not A Factor in the new Common Measures, but of value in Statistical Model Barriers to Employment Statute incredibly Specific about these Elements Statute requires Statewide Reports to break all data out by Barrier to Employment 14

15 Barriers to Employment WIOA requires reporting detailed information on Barriers to Employment on ALL Participants Displaced Homemakers Low-income individuals Indians, Alaska Natives, and Native Hawaiians Individuals with disabilities Older individuals Ex-offenders (which now means pretty much anyone arrested) Homeless individuals Foster Youth or those who have aged out of the foster care system Individuals who Are English Language Learners, Have Low Levels of Literacy, and May face Substantial Cultural Barriers Eligible migrant and seasonal farmworkers (and their dependents) Individuals within 2 years of exhausting lifetime eligibility for TANF Single parents (including single pregnant women) Long-term unemployed individuals Other groups identified by the Governor 15

16 Intent of Statistical Models Estimate the level of performance that should be achieved for a given Casemix & set of Economic Conditions Developed using historic data to establish norms Each Measure has its own Statistical Model Models estimate the impact that each factor has on an outcome Essentially if the % of the Casemix that is were to increase by X%, how much is the outcome likely to go up or down? All Probability Based 16

17 Statistical Model Status Statistical Models Cannot Be Built without DATA Modeling analyzes prior case mixes, economic conditions & WIOA outcomes to estimate the impact of each demographic & economic factor RSA has Participant data but no WIOA outcomes OCTAE has no data DOL had lots of Participant data and was able to use the data to approximate WIOA outcomes Their Statistical Work was Strong but Results Limited WIOA changed Exit Rules but DOL couldn t simulate that DOL & ED Changed the Credential Rate methodology after Statistical Model was developed DOL was able to create a state adjustment factor but not a Board adjustment factor Models >10 Points off Actual Performance ~80% of the time 17

18 Adjustment Factors? A Model generally estimates the average impact that a factor has Not all States are the Same Drop in Movie Ticket Sales hurts California more than Texas Drop in Oil Prices hurts Texas more than Maine ESL has lesser impact in Texas than Michigan Building separate models for each state is HUGE undertaking Adjustment Factor is a way to push targets up or down slightly to reflect differences between the states Not an ideal solution but not a bad one Unfortunately, DOL s models have no Board adjustment factors Dallas very different than Cameron County, right? 18

19 DOL Statistical Models Not All Models have the Same # of Factors (or the Same Factors) Adult Employed Q2 Post-Exit has 50 Factors DW Employed Q2 Post-Exit has 50 Factors Youth Employed/Enrolled Q2 Post Exit has 51 Factors WP Employed Q2 Post-Exit has 31 Factors Some of the Difference is due to Data Availability Even when Models have a Common Factor, the impact of the Factor can be Different Impact of a 1 point increase in % of Participants who are TANF Recipients: points for Adult points for DW points for Youth 19

20 Adult Employed Q2 Post-Exit Model There are 50 Factors in this Model 12 for Participant Demographics (sex, age, race, ethnicity) 6 for Participant Education Level 4 Related to Pre-Participation Employment 9 Related to Barriers for Employment 7 Related to Types of Services Provided 9 Related to Economic Factors 3 Miscellaneous Factors 20

21 Demographics with Biggest Impact Age 66+ Demographic Factor Associate s Degree More than One Race Limited English Proficiency Asian Less than High School Earnings in Q2 Pre-Participation Individuals with Disability General Impact of a 1 Point Increase in a Demographic Factor on Adult Employment Q2 Post-Exit Results -.68 Points +.32 Points -.31 Points -.23 Points +.21 Points -.17 Points +.15 Points -.14 Points Serving Higher % of Participants with Barriers to Employment will Reduce Performance Target Failure to Document Barriers will generally result in HIGHER Targets 21

22 Economic Factors with Biggest Impact Economic Factor Employment in Other Services Sector Unemployment Rate Employment in Construction Employment in Information, Finance, or Technology Employment in Leisure, Hospitality, or Entertainment General Impact of a 1 Point Increase in an Economic Factor on Adult Employment Q2 Post-Exit Results Points Points Points +.62 Points -.61 Points Bad Economy will generally Reduce Performance Target 22

23 WIOA National Regression Model Details Adult Employed Q2 Post-Exit BOARD HISTORY/MODEL RESULTS/TARGET PROPOSAL Rate Num Den From To BCY % Q3 2012Q2 BCY % Q3 2013Q2 BCY % Q3 2014Q2 LAST YEAR - BCY % Q3 2015Q2 Last R4Q 74.32% Q1 2015Q4 THIS YEAR - Preview BCY17 (2 Qtrs) 75.00% Q3 2015Q4 BCY16 Regression "Target" (Based on an "Average State") 86.61% NA NA 2014Q3 2015Q2 BCY16 Regression "Prediction" (Predicted Texas Level) 85.72% NA NA 2014Q3 2015Q2 BCY17 Regression "Target" (Based on an "Average State") 85.48% NA NA 2015Q3 2016Q2 BCY17 Regression "Prediction" (Predicted Texas Level) 84.59% NA NA 2015Q3 2016Q2 Estimated Cumulative Impact of Casemix & Economic Changes from BCY16 to BCY % NA NA NA NA BCY17's Expected Performance based on BCY16 Actual Performance Adjusted for Estimated Cumulative Impact of Casemix & Economic 77.52% NA NA NA NA Changes TWC Proposed BCY17 Target 77.5% NA NA 2015Q3 2016Q2 Factor Impact of a 1 Point Increase in the Factor Factor % Last Year Factor % This Year Change between THIS & LAST Year Predicted Impact of Change from Last Year to This Year Female % 70.93% 15.87% 1.27% Age 26 to % 35.47% -6.11% -0.03% Age 36 to % 27.91% 0.38% 0.02% Age 46 to % 13.37% 2.14% -0.02% Age 56 to % 5.23% 1.86% -0.34% Age % 0.00% 0.00% 0.00% Hispanic ethnicity % 36.05% 1.78% -0.06% Race: Asian (not Hispanic) % 6.40% -0.35% -0.07% Race: Black (not Hispanic) % 33.72% 4.51% -0.31% Race: Hawaiian/Pacific Islander (not Hispanic) % 0.00% 0.00% 0.00% Race: American Indian or Native Alaskan(not Hispanic) % 1.74% -1.63% 0.07% Race: More than one (not Hispanic) % 8.14% 0.84% -0.26% Highest grade completed: Less than High School graduate % 3.49% 1.80% -0.31% Highest grade completed: High school equivalency % 37.79% 9.14% 0.14% Highest grade completed: Some college % 39.53% -8.78% -0.99% Highest grade completed: Certificate or Other Post-Secondary Degree % 0.00% -0.56% -0.03% Highest grade completed: Associate degree % 4.65% 2.40% 0.76% Highest grade completed: Bachelor degree % 12.79% -2.38% -0.12% Employed at participation % 23.84% -9.31% -0.51% Individual with a disability % 5.23% 0.18% -0.02% Veteran % 4.07% -2.67% -0.05% Had earnings in 2nd and 3rd preprogram quarters % 51.74% -3.31% 0.12% Had earnings in 3rd preprogram quarter % 59.88% -2.48% 0.20% Had earnings in 2nd preprogram quarter % 61.63% 1.52% 0.23% Received services financially assisted under the Wagner-Peyser Act % 88.37% -3.76% 0.04% Limited English-language proficiency % 2.91% -4.40% -1.02% Single parent % 44.77% 7.69% 0.39% Low income % 87.21% 7.43% 0.04% TANF recipient % 0.00% 0.00% 0.00% Other public assistance recipient % 65.12% 11.75% -0.22% Homeless % 2.91% 0.10% -0.01% Offender % 25.00% 5.90% -0.15% UI claimant, non-exhaustee % 13.37% 4.95% 0.07% UI exhaustee % 3.49% 0.68% -0.01% Received supportive services % 0.00% -1.12% -0.04% Received needs-related payments % 0.00% -0.56% 0.03% Received intensive services % 97.09% -2.91% 0.00% Received training services % 46.51% % -1.92% Established Individual Training Account (ITA) % 50.00% % 1.13% Pell grant recipient % 2.91% -1.03% 0.06% Received pre-vocational activity services % 0.00% 0.00% 0.00% Natural Resources Employment % 0.36% -0.04% 0.01% Construction Employment % 5.63% 0.15% -0.19% Manufacturing Related Employment % 5.59% -0.32% 0.01% Information, Finance, or Technology Employment % 29.14% 0.38% 0.23% Educational, or Health Care Related Employment % 19.80% -0.31% 0.07% Leisure, Hospitality, or Entertainment Related Employment % 12.76% 0.38% -0.23% Other Services Employment % 3.63% -0.06% 0.09% Federal, State, or Local Government Employment % 7.06% -0.24% 0.09% Unemployment Rate Not Seasonally Adjusted % 3.10% -0.52% 0.73% Average Effect NA NA NA NA Texas State Effect NA NA NA NA

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