Overview

This document provides an overview of the analyses conducted in developing and evaluating the statistical adjustment models that will be used for Program Years (PYs) 2024 and 2025. Beginning with PY 2020, the statistical models were used as a factor in performance negotiations and to assess state performance after each program year in accordance with WIOA performance accountability provisions for the following performance indicators and WIOA title I and title III programs: Employment Rate in the Second Quarter after Exit (ERQ2), Median Earnings in the Second Quarter after Exit (MEQ2), and Measurable Skill Gains Rate (MSG) for the Adult, Dislocated Worker, Youth, and Wagner-Peyser programs (note: the MSG indicator does not apply to the Wagner-Peyser program). The performance accountability provisions were fully implemented to also include the Employment Rate in the Fourth Quarter after Exit (ERQ4) and Credential Attainment (CRED) indicators starting with PY 2022.

The initial methodology of the statistical adjustment models was developed by the U.S. Department of Labor’s Chief Evaluation Office in 2016. The models recommended at that time were implemented as a preliminary test beginning with PY 2017 using WIA data and proxy variables where necessary. Subsequently, the models have undergone revisions and enhancements with each negotiations cycle. For more information on previous versions of the models, see the corresponding Model Summary Reports for those cycles on the State Performance Negotiations Resource Archive or click on the reports that are linked below.

  • WIOA Statistical Adjustment Model Methodology Report - the initial proposed methodology for the WIOA Statistical Adjustment Models. This report provides background on the WIOA requirement of a statistical adjustment model, program and data limitations, a framework for identifying an appropriate model, and a recommended approach to be implemented for future WIOA program years.
  • PY 2020-2021 Model Selection Report - the description of the models chosen for Program Years 2020-2021. In addition to the final model estimates and specifications, this report details the main modifications made at that time which included: how model performance is affected by the data (WIA vs. WIOA), refinement of the model specifications, development of the Measurable Skill Gains models, and other changes.
  • PY 2022 - 2023 Model Selection Report - the description of the models chosen for Program Years 2022-2023. In addition to the final model estimates and specifications, this report details the main modifications made at that time which included: changing how the economic conditions data is aligned to the WIOA participant data to better measure the effects of the economy on performance, implementing the use of random sample groups of participant data for the Measurable Skill Gains models, adding the Employment Rate 4th Quarter after Exit and Credential Attainment models, and other changes.

The models for Program Years 2024-2025 have been further refined and this document explains the changes that have been made for this negotiations cycle and provides the final model specifications and estimates. The updated models use the reported WIOA data from PY 2018-2022. For in-depth details of the changes made to these versions of the models see the Modifications tab.

This report includes the following sections:

  • Modifications - details on the changes made to the this version of the models
  • Final Estimates - the final model estimates (i.e., the coefficients) for PY 2024-2025
  • Final Predictions - the final model predictions used in PY 2024-2025 (i.e., Pre-Program Year Performance Estimate)
  • Prediction Plots - plots showing how the PY 2024-2025 predictions compare to actual PY 2022 performance
  • Full Variable Table - a table showing which variables are in included in each model

Modifications

The information below details what aspects of the models were changed for PYs 2024-2025. The changes are part of the Departments of Labor and Education’s efforts to continuously improve the statistical adjustment models and are informed by having more available WIOA data, economic changes, stakeholder feedback, and increasing clarity and refinement in the modeling approaches.

Accounting for Wage Growth/Inflation

Accounting for State Labor Force Wage Growth

This section gives an overview of the changes that were made by adding a variable to account for wage changes in the labor force over time.

Background and motivation

The previous versions of the statistical adjustment models did not include any variable that accounted for the level of wages within a state. The only economic condition variables were variables that estimate the unemployment rate and share of different industries within a state. As a result, for the Median Earnings 2nd Quarter after Exit (MEQ2) models, the relative level of earnings within a state was only accounted for within a state’s fixed effect value and the change in wages within a state’s economy was not accounted for when applying the model for performance assessments.

Data from past program years illuminates how the models systematically predicted lower values for MEQ2 than the actual results. As the plot below shows, the predicted values for a future program year are lower than the actual outcome values—the points should be centered around the line meaning that the model predicts values above and below the actual level in equal amounts. In addition, the residual (i.e., difference between the predicted and actual values) increases as wages go up.

Median Earnings 2nd Quarter after Exit Increasing Over Time

Given the model results shown in the plot above, we wanted to consider adding an economic conditions variable that accounted for the change in wages over time. The main idea being that average/median wages of the labor force are likely increasing over time so the MEQ2 outcome should be increasing over time as well, regardless of performance. We see this in the data as shown below. For all WIOA title I and III programs, the average level of the MEQ2 has increased each program year.

MEQ2 Compared to QCEW Wage Data

To get a measure of the wages in the economy we use data from the Quarterly Census of Employment and Wages (QCEW) survey from the Bureau of Labor Statistics. This data source is also used in the models for the industry share variables. For this variable we use the QCEW estimate of the total wages of the labor force by state for each quarter. We then take those total wages and divide it by the total labor force to get a measure of the average wage of the labor force.

The plot below shows the same data that is shown above combined with the QCEW average labor force wage that is aligned to the WIOA program year. As the plot shows, the average wages of the labor force has the same trend in growth over time. The average wages of the labor force (QCEW) are higher than the median earnings of the WIOA participants (MEQ2), which is likely due to WIOA participants being a harder to serve population on average and differences in collection of the wage values. However, what is important is the trend of the increase which is comparable and aligned for both data types.

It could be the case that while there is an overall average increase for all states, there is some variance across states. In fact, some states could have a different relationship. To check for this possibility we also looked at the relationship for each state individually. The plot below shows the same plot as the one above except for each state individually. The trend of parallel increases for both the MEQ2 outcome and QCEW average labor force wages is present across all states.

Testing the Inclusion of Average State Wages in the Model

Including this variable in the MEQ2 models was tested to see if its inclusion improved the MEQ2 models. Results from those tests of the final MEQ2 models with average state wages variable included and the final without the average state wages included are show below. The first table shows the summary statistics of the two model types. Across all programs and measures of model performance the MEQ2 models with a variable of average state wages performed better.

The plots below show the variables of importance in the models. The inclusion of the variable state_avg_wages becomes the most important variable in the models. Also, the models become less reliant on the variable wagesprior which is a positive result given that variable uses data from a reported WIOA PIRL element that often has high variance. In addition, we see some state fixed effects drop off the list of important variables, which is a desired result because as a general principle we prefer to minimize the state fixed effects (i.e., the fixed effects do not have an impact on the use of the models for performance assessments). Clearly, the average state wage variables is capturing a good portion of the wage changes within a state economy.

Adding State Average Wages to the MEQ2 Models

Given the positive analyses and test results, the average state wages variable is included in the MEQ2 models for PYs 2024-2025. However, the actual average wage value is not used. Instead the values were normalized using a min-max normalization to create values between 0 and 1. This process is described in more detail in the next tab.

Normalizing Variables

Normalizing Model Variables

There has been some desire to normalize the non-percentage variables in the models (i.e., the participant characteristics variables daysinprog, daysenrolled, and wagesprior) given that they are on a different scale than the other variables that are percentages. This desire was amplified with the inclusion of the economic conditions variable state_avg_wages. As a result, all four of these variables are normalized in the models for PYs 2024-2025.

The reason for making this change is driven by two primary factors. First, having some of the variables on a different scale makes it more difficult to interpret the coefficients and get a sense of their relative weights. Having all the variables on the same scale results in coefficients that can be more easily compared. Second, there is a model performance improvement in having the variables on the same scale and large variance or extreme values have less of an impact. When the values are normalized the model is using relative change in the values instead of the absolute change in the values. This can temper some of the effects when a state may be an outlier with unusually high values for daysinprog or wagesprior.

Normalization Method

The normalization method used is Min-Max normalization. This method converts all the values into a scale from 0 to 1 which aligns with most of the other model variables which are percentages and thus on the same scale.

Getting the Minimum and Maximum Values

The minimum and maximum values were obtained by getting the total data (i.e., data from PY 2018 - 2022) and then capturing the min-max values of the total data by program.

There is a slight variation in min-max values for the state_avg_wages variable. Unlike the other variables, which use the minimum and maximum for the variable from the total data, the state_avg_wages uses the min-max values within the state. In other words, the data is first grouped by state and then the min and max value for each state for state_avg_wages is used. This is because the value of the variable is to capture wage changes in the state rather than get the relative wages of a state compared to other states.

Table of Minimum and Maximum Values

A table with all the minimum and maximum values for the variables where normalization was applied are shown in the table below. The table can be exported as desired.

The data in the table can be used to convert raw values into normalized values or normalized values back to the raw values. For example, if you had a wagesprior value of 6,500 for the Adult program and MEQ2 Indicator you could normalize that value to the scale that was used for the PYs 2024-2025 models. If you look at the data in the table below, that variable had a minimum value of 1,273 and a maximum value of 11,717. Applying the min-max formula using those values gives a normalized value of 0.505. Likewise, if you had a normalized value of 0.7 for the same program and indicator you could apply the formula to get the original raw value of 8,583.

How Normalization was Applied

As described above, all the variables used in the models that are not percentages (i.e., had a default value from 0 to 1) were normalized. This method was applied to all the data used to fit the models and for the prior values used to get the the predicted outcomes (Pre-Program Year Performance Estimate) in PY 2024. The normalization method will also be applied to the actual values when the models are applied for the performance assessments. The actual values will be normalized at the same scale by using the min max values in the table above.

Definition Change in Race Variables

The race variables were changed to exclusive categories. In other words, if a participant indicated only one race they are counted in that race grouping and if they indicated more than one race category then they are counted only in the Race: Multiple variable. As a result, the total percentages of all race categories add up to 100% (Note: Since Race: White is treated as the reference category by the model, it does not have a coefficient is therefore not displayed in the tables).

Previous versions of the models followed the standard WIOA reporting by counting an individual for every race category they identified. The result of using this methodology was that every participant included in Race: Multiple was also included in two or more other racial variables.

This change was made because there are a few states with relatively high levels of participants in the Race: Multiple category, however this change has minimal effect for most states. This change will minimize potential unexpected impacts during assessments for those states if there are shifts in the demographics of the participants served and/or there are changes in how the race information is reported.

Prediction Test

The plots below show the performance of the selected PY 2024-2025 models in predicting actual PY 2022 performance. This is just a test of the current models to see how they perform when predicting the outcomes of the most recently reported program year.

Adult

Dislocated Worker

Youth

Wagner-Peyser

Final Estimates

This section has tables that provide the coefficients for each variable in each program indicator model. The last tab (All Model Estimates) has the complete data for all models and the table can be sorted, searched, and exported.

Adult

Term Employment Rate 2nd Quarter after Exit Median Earnings 2nd Quarter after Exit Employment Rate 4th Quarter after Exit Credential Attainment Measurable Skill Gains
female -0.057 -2305 0.035 -0.050 -0.190
age2544 -0.105 -195 -0.169 0.064 -0.138
age4554 -0.156 -665 -0.347 -0.044 -0.365
age5559 -0.271 -2354 -0.357 -0.055 -0.369
age60 -0.417 3012 -0.569 -0.278 -0.267
hispanic -0.116 -35 -0.135 0.035 -0.105
raceasian 0.296 3755 -0.155 -0.023 -0.127
raceblack -0.063 -1158 -0.112 -0.046 0.156
racehpi -0.617 4099 -0.130 -0.262 0.064
raceai -0.018 -1015 -0.092 -0.087 -0.663
racemulti -0.189 3544 0.072 -0.132 -0.313
hsgrad 0.106 476 0.084 0.266 0.160
collegedropout -0.018 -385 0.056 0.220 0.160
certotherps -0.167 -2025 0.019 0.448 -0.190
associate 0.358 -111 0.211 0.278 0.308
ba -0.009 3098 0.184 -0.043 0.062
gradschool 0.277 10587 0.295 0.080 0.464
empentry 0.101 1540 0.051 0.124 0.362
edstatentry 0.110 3600 0.063 -0.118 -0.019
disabled -0.224 -681 -0.154 -0.067 -0.181
veteran -0.281 1206 -0.239 0.254 0.079
englearner -0.109 -1057 -0.054 0.000 0.280
singleparent 0.059 1226 0.039 -0.050 0.060
lowinc -0.035 -433 -0.058 0.092 -0.049
homeless -0.045 -865 -0.197 -0.200
offender 0.071 276 0.052 0.125 0.104
dishomemaker -0.291 -2506 -0.100 -0.397
recwagesprior 0.207 -7 0.146 -0.092 0.068
longtermunemp 0.003 -1100 0.011 -0.118 0.353
uiclaimant 0.010 124 0.017 0.067 0.083
uiexhaustee 0.203 287 0.380 0.148
recotherasst 0.027 698 -0.014 -0.132 0.081
recssi 0.021 2578 -0.145 0.061 0.234
rectanf -0.227 -2135 -0.180 0.089 0.035
daysinprog_morethanone -0.027 903 0.001 0.094
natresources -1.983 14756 -4.492 1.257 -9.420
construction -2.555 -2198 -1.877 0.502 -0.323
manufacturing -0.296 -12444 -2.264 6.821 -5.944
information 1.807 -40739 -2.362 3.150 20.317
financial -5.213 -51355 -6.959 1.823 5.602
business -1.102 16272 -2.339 2.593 -2.313
edhealthcare -1.426 -18912 -1.934 0.722 -1.227
leisure -1.203 6793 -2.358 3.346 -6.716
otheremp -0.446 -24586 0.676 1.831 -16.891
publicadmin -1.315 38472 -2.297 5.709 -2.068
ur -1.132 -5160 -1.435 0.500 -3.074
wagesprior 1649
state_avg_wages 3767
daysenrolled -0.336
daysenrolled_under30 -0.390
AL 1.845 9227 2.782 -1.743 2.824
AK 1.936 6702 2.759 -1.631 3.102
AZ 2.040 10059 2.907 -1.556 2.408
AR 1.829 9493 2.741 -1.683 2.879
CA 1.904 9528 2.878 -1.682 2.537
CO 2.010 9652 2.896 -1.482 2.443
CT 1.977 12956 2.911 -1.663 2.506
DE 2.170 12887 3.052 -1.535 1.741
DC 2.005 1437 2.945 -2.592 3.509
FL 2.053 9693 2.894 -1.400 2.482
GA 1.875 9442 2.796 -1.620 2.248
HI 2.042 3925 2.683 -1.677 2.950
ID 1.978 7730 2.847 -1.669 2.867
IL 1.894 11280 2.850 -1.629 2.547
IN 1.830 10909 2.770 -1.963 3.192
IA 1.949 11443 2.859 -1.833 2.698
KS 1.919 9649 2.838 -1.729 2.704
KY 1.761 10218 2.651 -1.811 2.595
LA 1.906 9447 2.752 -1.442 2.798
ME 1.868 10078 2.727 -1.623 2.553
MD 2.029 7348 2.844 -1.634 2.526
MA 2.032 12023 2.941 -1.518 2.171
MI 1.893 10747 2.834 -1.712 2.841
MN 1.935 11689 2.813 -1.634 2.556
MS 1.864 9692 2.804 -1.785 2.870
MO 1.899 10668 2.806 -1.688 2.493
MT 1.887 7127 2.700 -1.554 3.108
NE 1.936 10577 2.860 -1.721 2.445
NV 1.997 6372 2.859 -1.577 3.391
NH 1.936 12062 2.786 -1.508 2.815
NJ 1.822 9832 2.672 -1.487 2.358
NM 1.978 7350 2.839 -1.547 3.071
NY 1.963 11334 2.865 -1.561 2.012
NC 1.881 9171 2.813 -1.820 2.571
ND 1.961 10533 2.823 -1.426 2.747
OH 1.927 11382 2.866 -1.764 2.892
OK 1.870 7202 2.768 -1.672 2.934
OR 1.841 9167 2.737 -1.616 2.968
PA 1.929 11066 2.813 -1.624 2.624
RI 2.096 10750 2.908 -1.656 2.733
SC 1.860 9055 2.811 -1.911 2.781
SD 1.984 9873 2.861 -1.615 2.858
TN 1.851 9606 2.759 -1.775 2.759
TX 1.969 10059 2.847 -1.467 2.636
UT 1.921 9136 2.790 -1.644 2.156
VT 1.895 9834 2.693 -1.659 2.830
VA 1.941 7516 2.832 -1.641 2.612
WA 1.744 9881 2.749 -1.529 2.073
WV 1.835 8238 2.694 -1.515 2.623
WI 1.793 10762 2.796 -2.002 2.781
WY 2.064 7137 2.894 -1.466 3.411
PR 2.042 826 2.768 -2.176 3.003

Dislocated Worker

Term Employment Rate 2nd Quarter after Exit Median Earnings 2nd Quarter after Exit Employment Rate 4th Quarter after Exit Credential Attainment Measurable Skill Gains
female -0.033 -3318 -0.011 -0.100 -0.139
age2544 -0.146 -222 -0.239 -0.004 -0.329
age4554 -0.173 -1351 -0.390 0.189 -0.378
age5559 -0.291 -1966 -0.317 0.053 -0.533
age60 -0.213 -4240 -0.445 -0.038 -0.502
hispanic -0.090 813 0.060 -0.035 0.237
raceasian 0.230 -414 0.416 -0.284 -0.177
raceblack -0.076 -801 -0.113 0.019 0.101
racehpi -0.255 -9488 0.124 0.162 0.202
raceai -0.042 -880 0.142 -0.088 0.382
racemulti -0.133 -4060 0.190 0.134 0.377
hsgrad 0.030 385 0.020 0.090 0.223
collegedropout 0.042 -775 -0.021 -0.057 0.124
certotherps -0.159 -2385 -0.227 0.258 0.142
associate -0.029 1995 -0.163 0.154 0.478
ba -0.073 4105 0.038 -0.060 0.115
gradschool 0.112 5944 -0.077 -0.040 0.186
empentry 0.026 640 0.094 0.188 0.174
edstatentry -0.007 3345 0.096 -0.039 0.112
disabled -0.121 -1557 -0.012 -0.213 -0.129
veteran -0.111 1114 -0.054 0.031 0.097
englearner -0.169 401 -0.101 0.196 0.079
singleparent 0.142 355 0.022 -0.094 0.094
lowinc -0.025 -532 -0.048 -0.049 -0.137
homeless 0.547 385 0.066 -0.339
offender -0.235 1891 -0.060 -0.037 0.142
dishomemaker -0.138 235 0.060 0.224
recwagesprior 0.036 1343 0.126 -0.078 0.083
longtermunemp -0.045 2274 -0.012 -0.075 0.079
uiclaimant 0.018 -260 0.023 0.071 0.016
uiexhaustee 0.088 -1564 0.094 0.023
recotherasst 0.161 1233 0.078 -0.065 -0.036
recssi -0.189 -1916 0.147 0.774 -0.711
rectanf -0.101 -1962 -0.022 -0.592 -0.377
daysinprog_morethanone 0.010 993 0.017 0.024
natresources 0.146 9883 -0.176 -4.862 -11.683
construction -1.878 56514 -1.978 -2.853 -5.275
manufacturing 0.743 50088 0.867 3.736 -8.165
information 2.122 30332 -2.621 -6.573 13.258
financial 1.684 57616 -1.680 2.355 0.151
business -0.507 73599 -0.426 3.608 -2.949
edhealthcare -0.108 23859 -1.016 2.209 -8.201
leisure 0.414 50857 -0.507 -1.943 -9.482
otheremp 0.619 -36211 2.117 6.812 -13.328
publicadmin -0.606 75766 -0.406 6.923 -5.370
ur -1.458 -6292 -1.326 -1.388 -2.344
wagesprior 4287
state_avg_wages 2886
daysenrolled -0.317
daysenrolled_under30 -0.372
AL 0.981 -30613 1.438 -0.874 6.110
AK 1.151 -25524 1.485 -0.918 6.196
AZ 1.024 -31090 1.546 -0.596 5.633
AR 0.995 -29118 1.475 -0.735 6.245
CA 0.945 -29809 1.452 -0.504 5.689
CO 1.008 -30666 1.603 -0.475 5.623
CT 0.962 -27321 1.572 -0.908 6.018
DE 0.893 -30219 1.625 -0.761 5.498
DC 1.124 -38930 1.490 -2.644 6.644
FL 1.135 -30260 1.656 -0.436 5.622
GA 1.025 -29041 1.594 -0.668 5.277
HI 0.988 -25507 1.341 -0.565 6.087
ID 1.045 -30441 1.494 -0.595 6.100
IL 0.964 -27868 1.496 -0.887 5.690
IN 0.918 -29454 1.371 -0.941 6.377
IA 0.915 -27456 1.492 -0.872 6.064
KS 0.997 -29511 1.483 -0.810 6.044
KY 0.915 -30436 1.379 -0.887 6.097
LA 0.993 -28709 1.554 -0.367 6.285
ME 0.985 -28128 1.525 -0.777 6.187
MD 1.161 -31859 1.665 -1.155 5.942
MA 0.962 -29265 1.592 -0.806 5.680
MI 1.053 -29714 1.521 -0.824 6.092
MN 0.978 -27343 1.530 -0.793 6.165
MS 0.934 -29367 1.483 -0.761 6.138
MO 0.922 -29294 1.503 -0.783 5.872
MT 0.912 -27899 1.389 -0.678 6.304
NE 1.023 -29631 1.582 -0.808 5.880
NV 1.074 -31088 1.593 0.082 6.280
NH 0.966 -27220 1.503 -0.718 5.953
NJ 0.861 -28146 1.405 -0.922 5.421
NM 1.015 -30513 1.504 -0.580 6.364
NY 0.788 -30007 1.479 -1.057 5.576
NC 0.950 -31513 1.488 -0.897 5.810
ND 1.053 -24122 1.590 -0.247 6.362
OH 0.993 -29221 1.553 -0.823 6.088
OK 0.967 -30022 1.440 -0.585 6.174
OR 0.883 -29335 1.342 -0.604 5.995
PA 1.024 -27706 1.560 -0.877 5.946
RI 1.058 -29696 1.625 -0.921 6.151
SC 1.012 -30532 1.528 -0.877 6.011
SD 0.919 -28393 1.481 -0.771 6.253
TN 1.010 -29331 1.518 -0.848 5.846
TX 0.976 -28395 1.530 -0.469 5.742
UT 0.988 -30116 1.502 -0.622 5.359
VT 1.005 -29622 1.473 -0.668 6.529
VA 1.136 -32171 1.640 -0.974 5.786
WA 0.878 -28316 1.469 -0.257 5.255
WV 0.999 -27948 1.533 -0.655 6.219
WI 0.935 -29409 1.428 -0.958 6.164
WY 1.149 -25816 1.610 -0.144 6.801
PR 1.048 -35247 1.237 -1.485 5.397

Youth

Term Employment Rate 2nd Quarter after Exit Median Earnings 2nd Quarter after Exit Employment Rate 4th Quarter after Exit Credential Attainment Measurable Skill Gains
female -0.107 -635 -0.098 0.012 0.023
hispanic -0.086 -18 0.002 -0.065 -0.148
raceasian 0.135 -228 -0.099 0.187 0.099
raceblack -0.065 -1330 -0.079 -0.054 -0.033
racehpi 0.216 -344 -0.341 0.163 -0.099
raceai -0.285 -1396 0.035 -0.039 0.055
racemulti -0.078 1275 -0.006 0.385 0.047
hsgrad 0.169 1323 0.130 0.175 0.050
collegedropout 0.026 -375 0.207 0.119 -0.911
certotherps -0.078 520 -0.200 -0.574 -0.616
empentry 0.257 1143 0.184 0.010 0.364
edstatentry 0.120 580 0.076 0.113 0.177
disabled -0.128 -39 -0.077 0.083
englearner -0.159 -109 -0.253 0.064
lowinc -0.022 167 -0.050 -0.001
homeless 0.139 -742 -0.073 0.045
offender -0.072 -645 -0.056 -0.042
longtermunemp -0.025 179 0.027 -0.035 0.170
uiclaimant 0.126 -389 -0.003 0.102
recotherasst 0.046 -160 -0.028 -0.046 -0.122
recssi 0.027 -2331 -0.008 0.190 0.527
rectanf 0.082 760 0.098 -0.175 0.206
daysinprog_morethanone 0.029 1261 0.044 0.010
natresources -3.511 -3236 -4.605 -2.446 -4.884
construction 1.011 14586 -0.890 6.408 2.894
manufacturing -1.148 9402 -3.670 0.696 -7.449
information 1.201 23072 -8.275 -14.983 4.688
financial -3.828 19434 -5.497 -4.682 8.190
business -1.362 38402 -3.232 -1.540 -2.246
edhealthcare -2.313 1985 -3.971 1.103 0.334
leisure -1.770 21143 -2.729 1.444 -3.823
otheremp -0.559 10670 -1.724 5.424 -9.714
publicadmin 0.169 21588 -1.025 2.925 -0.812
ur -1.173 -537 -1.034 0.056 -3.311
state_avg_wages 2181
daysenrolled -0.275
daysenrolled_under30 -0.004
age1415 0.009 -3606 -0.050 -0.545 0.304
age1617 -0.184 -3295 -0.058 -0.044 -0.024
age1819 -0.169 -2937 -0.148 0.005 -0.010
age2021 -0.323 -132 -0.247 -0.141 -0.058
associateorba 0.218 1032 -0.199 -0.691 -0.470
yparent 0.079 -1025 -0.108 -0.059 -0.059
basiclitdeficient 0.069 -105 0.041 0.005 -0.077
yfoster -0.122 -3288 0.114 -0.201
ynaa -0.017 193 0.008 0.045 -0.046
AL 2.059 -8180 3.517 0.012 1.977
AK 1.961 -7250 3.222 -0.319 1.626
AZ 2.138 -8609 3.548 0.296 1.423
AR 2.105 -7369 3.578 0.071 1.992
CA 2.138 -8943 3.707 0.470 1.869
CO 2.065 -9619 3.612 0.384 1.429
CT 2.248 -8216 3.815 0.516 1.781
DE 2.269 -9392 3.724 0.565 1.079
DC 1.745 -14576 3.421 -0.237 2.245
FL 2.174 -9331 3.574 0.425 1.529
GA 2.085 -8982 3.608 0.563 1.598
HI 1.978 -9686 3.346 -0.375 1.565
ID 2.119 -7658 3.569 -0.190 1.776
IL 2.123 -8615 3.641 0.433 1.813
IN 2.107 -7256 3.631 0.068 2.398
IA 2.138 -6909 3.673 0.089 1.649
KS 2.139 -8165 3.616 0.139 1.781
KY 1.963 -8030 3.426 0.064 1.828
LA 2.041 -7532 3.520 -0.097 1.548
ME 2.078 -7709 3.572 -0.107 1.517
MD 2.033 -10148 3.517 0.052 1.435
MA 2.199 -9195 3.805 0.461 1.328
MI 2.163 -8163 3.708 0.301 2.040
MN 2.138 -7167 3.720 0.224 1.498
MS 2.227 -7029 3.671 0.161 2.241
MO 2.149 -8014 3.682 0.257 1.521
MT 2.029 -7564 3.239 -0.463 1.330
NE 2.116 -8163 3.637 0.094 1.370
NV 2.065 -10207 3.365 -0.155 1.876
NH 2.073 -7362 3.603 0.305 1.821
NJ 2.094 -9010 3.464 0.347 1.746
NM 2.153 -8594 3.536 -0.116 1.610
NY 2.169 -8897 3.765 0.496 1.098
NC 2.074 -9098 3.607 0.161 1.714
ND 2.164 -4703 3.504 -0.005 1.456
OH 2.159 -8388 3.703 0.189 1.934
OK 2.094 -8292 3.524 -0.006 1.951
OR 2.039 -7375 3.495 0.083 1.913
PA 2.139 -8332 3.627 0.237 1.831
RI 2.282 -8790 3.732 0.171 1.554
SC 2.089 -8580 3.593 0.178 2.019
SD 2.193 -6865 3.584 -0.122 1.363
TN 2.075 -8367 3.586 0.248 1.890
TX 2.150 -8369 3.634 0.236 1.676
UT 2.061 -8121 3.533 0.275 1.394
VT 2.149 -6459 3.532 -0.242 1.683
VA 2.038 -11004 3.599 0.249 1.754
WA 1.943 -7970 3.652 0.541 1.480
WV 2.029 -7936 3.454 -0.062 1.623
WI 2.092 -7429 3.688 0.121 2.007
WY 2.197 -6364 3.465 -0.178 1.761
PR 2.050 -11069 3.352 -0.293 1.955

Wagner-Peyser

Term Employment Rate 2nd Quarter after Exit Median Earnings 2nd Quarter after Exit Employment Rate 4th Quarter after Exit
female -0.027 -5148 0.154
age2544 -0.460 785 -0.442
age4554 -0.529 -2430 -0.485
age5559 -0.169 -867 -0.255
age60 -0.483 1793 -0.466
hispanic -0.072 -23 0.146
raceasian -0.179 -2040 -0.163
raceblack -0.147 -4160 -0.046
racehpi -2.653 -2289 -2.860
raceai -0.145 -925 -0.328
racemulti -0.307 1517 0.136
hsgrad 0.084 260 0.033
collegedropout -0.118 1337 0.048
certotherps 0.023 -2654 0.278
associate 0.145 1192 -0.042
ba -0.257 2126 -0.258
gradschool -0.180 -22 0.648
empentry 0.176 554 0.059
edstatentry -0.049 1686 0.000
disabled -0.010 -905 -0.095
veteran 0.086 441 0.041
englearner -0.045 379 -0.086
singleparent 0.044 426 0.228
lowinc 0.009 374 0.010
homeless -0.080 -5355 -0.217
offender 0.019 1658 -0.130
dishomemaker -0.199 -2330 -0.421
recwagesprior 0.503 348 0.161
longtermunemp -0.001 -49 -0.093
uiclaimant -0.013 -61 0.011
uiexhaustee -0.028 283 -0.015
recotherasst 1.168 7344 0.286
recssi -0.179 980 0.535
rectanf 0.028 -4783 -0.277
daysinprog_morethanone 0.018 1228 0.083
natresources -1.537 9791 -4.408
construction -0.568 -8377 -0.615
manufacturing -2.385 -16312 -2.207
information 6.857 44910 1.062
financial -6.320 -73974 -4.773
business -1.606 19297 -3.352
edhealthcare -1.951 -32068 -1.713
leisure -0.679 -9033 -2.457
otheremp -4.400 -48810 -2.320
publicadmin -2.753 -15199 -2.422
ur -1.771 -6361 -2.476
wagesprior 4650
state_avg_wages 2626
AL 2.330 19682 2.741
AK 2.317 18720 2.854
AZ 2.391 18832 2.806
AR 2.304 19224 2.717
CA 2.149 17293 2.744
CO 2.181 17334 2.782
CT 2.418 22589 2.815
DE 2.589 24309 2.922
DC 2.786 20289 3.234
FL 2.334 19323 2.785
GA 2.206 18614 2.787
HI 2.622 18547 3.190
ID 2.255 17044 2.792
IL 2.366 19624 2.852
IN 2.357 20131 2.786
IA 2.413 21227 2.790
KS 2.363 18952 2.764
KY 2.176 18929 2.667
LA 2.216 20055 2.694
ME 2.300 19482 2.729
MD 2.371 19639 2.847
MA 2.350 20324 2.848
MI 2.383 19111 2.859
MN 2.435 20747 2.834
MS 2.369 21163 2.794
MO 2.283 19535 2.757
MT 2.196 19068 2.750
NE 2.351 19862 2.820
NV 2.206 16710 2.950
NH 2.238 19423 2.669
NJ 2.265 19263 2.667
NM 2.203 17209 2.785
NY 2.405 21962 2.834
NC 2.343 19636 2.838
ND 2.210 19017 2.761
OH 2.414 19963 2.861
OK 2.221 17304 2.801
OR 2.228 17341 2.811
PA 2.346 20158 2.802
RI 2.507 21914 2.924
SC 2.292 19720 2.799
SD 2.375 21210 2.799
TN 2.263 18863 2.764
TX 2.293 18782 2.746
UT 2.230 17827 2.784
VT 2.199 19201 2.622
VA 2.330 17989 2.916
WA 2.053 16581 2.770
WV 2.185 17962 2.737
WI 2.333 19655 2.770
WY 2.159 17660 2.859
PR 2.476 14191 2.422

All Model Estimates

Final Predictions

The tables below show the predicted outcomes (Pre-Program Year Performance Estimate) in PY 2024 for each program indicator. The predictions are calculated by applying the model estimates to the most recent reported PY data on each state’s participant characteristics for each program indicator (i.e., PY 2022) and the most recent economic conditions data for states (i.e., time period 7/1/2022 - 6/30/2023).

Adult

State Employment Rate 2nd Quarter after Exit Median Earnings 2nd Quarter after Exit Employment Rate 4th Quarter after Exit Credential Attainment Measurable Skill Gains
AK 0.800 12397 0.853 0.707 0.731
AL 0.865 8728 0.865 0.816 0.757
AR 0.785 7919 0.788 0.715 0.767
AZ 0.746 8993 0.721 0.701 0.689
CA 0.701 8091 0.690 0.652 0.600
CO 0.762 9582 0.758 0.774 0.673
CT 0.737 7832 0.724 0.686 0.670
DC 0.729 10708 0.741 0.599 0.816
DE 0.828 8855 0.798 0.657 0.254
FL 0.892 10215 0.735 0.797 0.760
GA 0.840 8923 0.842 0.735 0.589
HI 0.766 8560 0.767 0.404 0.469
IA 0.779 7671 0.798 0.700 0.605
ID 0.767 8311 0.737 0.594 0.590
IL 0.789 9164 0.814 0.785 0.594
IN 0.801 8661 0.795 0.707 0.732
KS 0.786 8673 0.784 0.769 0.669
KY 0.766 8722 0.758 0.653 0.560
LA 0.728 8232 0.735 0.723 0.630
MA 0.732 7946 0.755 0.683 0.466
MD 0.829 8824 0.813 0.656 0.727
ME 0.716 7904 0.714 0.654 0.539
MI 0.858 9304 0.843 0.867 0.586
MN 0.745 9877 0.722 0.729 0.705
MO 0.780 8727 0.782 0.657 0.637
MS 0.903 8750 0.926 0.671 0.670
MT 0.694 7928 0.710 0.512 0.554
NC 0.800 8334 0.800 0.595 0.680
ND 0.794 9997 0.739 0.694 0.626
NE 0.809 9173 0.801 0.652 0.627
NH 0.806 9247 0.806 0.850 0.832
NJ 0.701 7314 0.685 0.637 0.643
NM 0.824 10044 0.817 0.621 0.784
NV 0.780 7952 0.748 0.760 0.758
NY 0.675 7907 0.663 0.523 0.658
OH 0.796 8375 0.797 0.720 0.688
OK 0.721 7677 0.729 0.705 0.692
OR 0.681 8796 0.691 0.650 0.636
PA 0.778 8123 0.771 0.711 0.621
PR 0.556 4343 0.558 0.532 0.545
RI 0.853 9557 0.898 0.776 0.434
SC 0.811 7939 0.800 0.644 0.663
SD 0.733 8035 0.727 0.673 0.629
TN 0.855 8756 0.847 0.711 0.750
TX 0.745 8453 0.754 0.724 0.695
UT 0.731 9162 0.752 0.714 0.457
VA 0.830 8240 0.806 0.724 0.726
VT 0.740 7956 0.651 0.594 0.643
WA 0.646 10471 0.645 0.772 0.490
WI 0.765 8402 0.779 0.631 0.551
WV 0.736 8253 0.744 0.793 0.467
WY 0.749 9149 0.725 0.695 0.886

Dislocated Worker

State Employment Rate 2nd Quarter after Exit Median Earnings 2nd Quarter after Exit Employment Rate 4th Quarter after Exit Credential Attainment Measurable Skill Gains
AK 0.890 14512 0.847 0.622 0.799
AL 0.798 10052 0.828 0.781 0.759
AR 0.822 9113 0.822 0.694 0.756
AZ 0.776 9698 0.740 0.658 0.653
CA 0.726 9592 0.730 0.713 0.573
CO 0.764 11681 0.790 0.807 0.641
CT 0.777 10262 0.794 0.670 0.674
DC 0.785 12289 0.780 0.420 0.790
DE 0.725 10207 0.782 0.681 0.180
FL 0.877 11157 0.862 0.831 0.686
GA 0.845 10149 0.853 0.763 0.569
HI 0.780 10605 0.793 0.490 0.471
IA 0.824 11265 0.867 0.685 0.563
ID 0.771 9621 0.773 0.590 0.524
IL 0.805 11855 0.828 0.740 0.638
IN 0.764 9162 0.768 0.736 0.741
KS 0.838 11155 0.857 0.793 0.676
KY 0.772 9951 0.760 0.645 0.746
LA 0.735 8927 0.739 0.890 0.639
MA 0.787 12267 0.811 0.704 0.468
MD 0.831 10616 0.853 0.668 0.772
ME 0.753 9426 0.762 0.611 0.525
MI 0.887 10170 0.888 0.823 0.638
MN 0.810 14118 0.829 0.797 0.758
MO 0.791 9608 0.794 0.685 0.693
MS 0.788 7556 0.800 0.706 0.668
MT 0.753 11234 0.734 0.537 0.474
NC 0.745 8872 0.743 0.632 0.738
ND 0.841 14893 0.854 0.815 0.773
NE 0.843 10917 0.862 0.666 0.679
NH 0.807 12277 0.743 0.898 0.570
NJ 0.643 9272 0.673 0.717 0.707
NM 0.737 8898 0.739 0.632 0.769
NV 0.790 9702 0.792 0.820 0.774
NY 0.654 8230 0.672 0.488 0.609
OH 0.805 9754 0.795 0.715 0.622
OK 0.752 10363 0.730 0.783 0.664
OR 0.671 8934 0.670 0.663 0.562
PA 0.824 10177 0.824 0.745 0.601
PR 0.593 4710 0.597 0.626 0.452
RI 0.866 11205 0.919 0.715 0.569
SC 0.828 9963 0.826 0.696 0.677
SD 0.749 9204 0.764 0.665 0.697
TN 0.861 9228 0.854 0.666 0.722
TX 0.736 10886 0.768 0.786 0.802
UT 0.794 11644 0.804 0.682 0.418
VA 0.860 10374 0.854 0.738 0.685
VT 0.906 8948 0.812 0.729 0.640
WA 0.684 12214 0.678 0.771 0.457
WI 0.827 10441 0.844 0.648 0.671
WV 0.795 11800 0.854 0.832 0.443
WY 0.872 14408 0.822 0.637 0.849

Youth

State Employment Rate 2nd Quarter after Exit Median Earnings 2nd Quarter after Exit Employment Rate 4th Quarter after Exit Credential Attainment Measurable Skill Gains
AK 0.623 4998 0.613 0.560 0.683
AL 0.759 4302 0.736 0.486 0.555
AR 0.779 4470 0.800 0.608 0.712
AZ 0.778 6188 0.767 0.656 0.638
CA 0.738 4948 0.718 0.608 0.664
CO 0.702 5360 0.721 0.599 0.630
CT 0.822 4599 0.798 0.799 0.790
DC 0.724 6836 0.618 0.543 0.592
DE 0.703 2667 0.801 0.748 0.618
FL 0.830 5096 0.819 0.734 0.680
GA 0.798 4032 0.800 0.656 0.521
HI 0.671 5024 0.684 0.622 0.296
IA 0.772 4658 0.765 0.657 0.497
ID 0.795 5627 0.823 0.431 0.620
IL 0.763 5090 0.780 0.718 0.608
IN 0.817 4644 0.853 0.691 0.742
KS 0.782 4484 0.782 0.645 0.494
KY 0.688 5035 0.731 0.601 0.490
LA 0.754 4678 0.790 0.655 0.522
MA 0.740 4959 0.717 0.626 0.452
MD 0.820 5164 0.793 0.683 0.608
ME 0.676 4845 0.751 0.537 0.522
MI 0.815 5106 0.815 0.701 0.511
MN 0.732 5521 0.814 0.699 0.575
MO 0.821 4861 0.794 0.603 0.597
MS 0.868 4040 0.892 0.762 0.779
MT 0.635 4669 0.586 0.285 0.343
NC 0.764 4950 0.751 0.496 0.591
ND 0.823 7204 0.805 0.594 0.596
NE 0.769 4837 0.808 0.509 0.486
NH 0.838 5982 0.842 0.779 0.699
NJ 0.680 3402 0.655 0.515 0.736
NM 0.704 4855 0.735 0.459 0.508
NV 0.701 4656 0.708 0.468 0.581
NY 0.656 3947 0.660 0.570 0.540
OH 0.740 4100 0.724 0.540 0.643
OK 0.765 5619 0.790 0.595 0.728
OR 0.669 5668 0.644 0.546 0.477
PA 0.708 4420 0.687 0.615 0.677
PR 0.578 2856 0.621 0.400 0.634
RI 0.798 4228 0.830 0.674 0.402
SC 0.840 5152 0.812 0.637 0.650
SD 0.762 4133 0.796 0.518 0.522
TN 0.841 5595 0.822 0.665 0.684
TX 0.743 5012 0.749 0.511 0.633
UT 0.773 4991 0.764 0.562 0.445
VA 0.789 4793 0.776 0.654 0.713
VT 0.650 5310 0.656 0.408 0.584
WA 0.605 5794 0.559 0.459 0.371
WI 0.830 4698 0.797 0.578 0.551
WV 0.650 4573 0.647 0.625 0.509
WY 0.744 4194 0.746 0.565 0.776

Wagner-Peyser

State Employment Rate 2nd Quarter after Exit Median Earnings 2nd Quarter after Exit Employment Rate 4th Quarter after Exit
AK 0.662 9265 0.678
AL 0.768 6969 0.748
AR 0.708 6905 0.701
AZ 0.651 8092 0.578
CA 0.633 8727 0.638
CO 0.591 8127 0.582
CT 0.641 8832 0.660
DC 0.576 7567 0.597
DE 0.657 7294 0.652
FL 0.682 7571 0.689
GA 0.631 7279 0.668
HI 0.574 8765 0.653
IA 0.699 8622 0.696
ID 0.702 8225 0.683
IL 0.658 8282 0.682
IN 0.753 8715 0.768
KS 0.721 8737 0.684
KY 0.608 7166 0.618
LA 0.623 6654 0.643
MA 0.637 9836 0.677
MD 0.642 8252 0.685
ME 0.665 8217 0.630
MI 0.734 9062 0.706
MN 0.590 9414 0.633
MO 0.708 7823 0.711
MS 0.795 6763 0.793
MT 0.677 7847 0.686
NC 0.692 7192 0.693
ND 0.631 9101 0.630
NE 0.744 8327 0.735
NH 0.719 10298 0.672
NJ 0.535 7964 0.596
NM 0.670 7170 0.655
NV 0.732 7936 0.716
NY 0.706 8246 0.701
OH 0.750 10168 0.698
OK 0.595 6840 0.602
OR 0.646 8841 0.639
PA 0.676 7958 0.685
PR 0.373 3680 0.354
RI 0.657 9406 0.694
SC 0.699 7081 0.672
SD 0.721 7174 0.705
TN 0.729 7889 0.690
TX 0.649 7773 0.661
UT 0.695 9977 0.712
VA 0.677 7516 0.709
VT 0.612 8632 0.558
WA 0.643 10130 0.628
WI 0.722 8267 0.733
WV 0.615 6958 0.637
WY 0.640 6786 0.587

All Predictions

Full Variable Table

This table is the full list of all the variables included in all the PY 2024-2025 models. An “x” indicates that the variable is included in that particular indicator model for that WIOA program.

Adult and Dislocated Worker
Youth
Wagner-Peyser
Full Variable Name ERQ2 ERQ4 MEQ2 CRED MSG Youth ERQ2 Youth ERQ4 Youth MEQ2 Youth CRED Youth MSG WP ERQ2 WP ERQ4 WP MEQ2
Female x x x x x x x x x x x x x
Age 14 to 15 x x x x x
Age 16 to 17 x x x x x
Age 18 to 19 x x x x x
Age 20 to 21 x x x x x
Age 25 to 44 x x x x x x x x
Age 45 to 54 x x x x x x x x
Age 55 to 59 x x x x x x x x
Age 60 or more x x x x x x x x
Hispanic Ethnicity x x x x x x x x x x x x x
Race: Asian x x x x x x x x x x x x x
Race: Black x x x x x x x x x x x x x
Race: Hawaiian or Pacific Islander x x x x x x x x x x x x x
Race: American Indian x x x x x x x x x x x x x
Race: Multiple x x x x x x x x x x x x x
Highest Grade Completed: High School Equivalency x x x x x x x x x x x x x
Highest Grade Completed: Some College x x x x x x x x x x x x x
Highest Grade Completed: Certificate or Other Post-Secondary Degree x x x x x x x x x x x x x
Highest Grade Completed: Associate Degree x x x x x x x x
Highest Grade Completed: Bachelor Degree x x x x x x x x
Highest Grade Completed: Associate or Bachelor Degree x x x x x
Highest Grade Completed: Graduate Degree x x x x x x x x
Employed at Program Entry x x x x x x x x x x x x x
In School at Program Entry x x x x x x x x x x x x x
Individual with a Disability x x x x x x x x x x x x
Veteran x x x x x x x x
Limited English Proficiency x x x x x x x x x x x x
Single Parent x x x x x x x x
Low Income x x x x x x x x x x x x
Homeless x x x x x x x x x x x
Individual who was Incarcerated x x x x x x x x x x x x
Displaced Homemaker x x x x x x x
Foster Care Youth x x x x
Youth Parent or Pregnant Youth x x x x x
Skills/Literacy Deficient at Program Entry x x x x x
Received Wages Prior to Participation x x x x x x x x
Wages Prior to Participation (Normalized) x x
Long-Term Unemployed at Program Entry x x x x x x x x x x x x x
UI Claimant x x x x x x x x x x x x
UI Exhaustee x x x x x x x
Received Other Public Assistance x x x x x x x x x x x x x
SSI or SSDI Recipient x x x x x x x x x x x x x
TANF Recipient x x x x x x x x x x x x x
Youth Needing Additional Assistance x x x x x
Median Days in Program (Normalized) x x x x x x x x x x x
Median Days Enrolled in Education or Training (Normalized) x x
Percent Enrolled in Education or Training Under 30 Days x x
Natural Resources Employment x x x x x x x x x x x x x
Construction Employment x x x x x x x x x x x x x
Manufacturing Employment x x x x x x x x x x x x x
Information Services Employment x x x x x x x x x x x x x
Financial Services Employment x x x x x x x x x x x x x
Professional and Business Services Employment x x x x x x x x x x x x x
Educational or Health Care Employment x x x x x x x x x x x x x
Leisure, Hospitality, or Entertainment Employment x x x x x x x x x x x x x
Other Services Employment x x x x x x x x x x x x x
Public Administration x x x x x x x x x x x x x
Average Wages of Labor Force (Normalized within State) x x x
Unemployment Rate Not Seasonally Adjusted x x x x x x x x x x x x x