Overview

This document provides an overview of the analysis that was conducted in developing and evaluating the statistical adjustment models that will be used for the 2022 and 2023 program years (PY). 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). Starting with PY 2022 the performance accountability provisions have been fully implemented to also include the Employment Rate in the Fourth Quarter after Exit (ERQ4) and Credential Attainment (CRED) indicators.

Previously, the statistical adjustment models underwent several rounds of development. The initial methodology of the statistical adjustment models was developed by the U.S. Department of Labor’s Chief Evaluation Office and is explained in the WIOA Statistical Adjustment Model Methodology Report. 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 model specifications (i.e., which variables were included in the models) changed slightly beginning in PY 2018 as a result of stakeholder feedback and ongoing statistical analysis by ETA. However, through PY 2019 the models had a limited role being used as a proxy method of assessing performance because of the structural differences in data collection between WIA and WIOA.

Beginning with PY 2020, there was sufficient WIOA data to generate usable model estimates for the ERQ2, MEQ2, and MSG indicators. As a result, the ERQ2 and MEQ2 models were refined and the MSG models were developed. The changes to the models during this period were largely limited to the selection of explanatory variables, the logic regarding how some variables were calculated, and removing anomalous data. The MSG indicator is a new performance indicator under WIOA (collection and reporting of this indicator began in PY 2016), so the models for this indicator were newly developed yet they follow the same basic framework as the previous models. For full details on the models developed for this period please see the PY 2020-2021 Model Selection Report.

The models for PY 2022-2023 were further refined prior to negotiations for those PYs and this document explains the changes that have been made and provides the final model specifications and estimates. As previously mentioned, this version of the models now includes models for the ERQ4 and CRED indicators for each WIOA program. The updated models also use the reported WIOA data from PY 2018-2020. For in-depth details of the changes made to this version of the models see the Modifications tab.

This report includes the following sections:

  • Modifications - details on the changes made to this version of the models
  • Final Estimates - the final model estimates (i.e., the coefficients) for PY 2022-2023
  • Final Predictions - the final model predictions used in PY 2022-2023 (i.e., Estimate0)
  • Prediction Plots - plots showing how the PY 2022-2023 predictions compare to actual PY 2020 performance
  • Full Variable Table - a table showing which variables were in included in each model

Modifications

The information below details what aspects of the models were modified for PY 2022-2023. The changes are part of efforts to continuously improve the statistical adjustment models and are informed by having more available WIOA data, economic changes (such as the impacts resulting from COVID-19), stakeholder feedback, and increasing clarity and refinement in the modeling approaches.

Better Account for Economic Changes

This section gives an overview of the changes that were made in the definition and calculation of the economic conditions variables.

Change in the alignment of the economic condition variables

Starting with the PY 2022-2023 models the economic condition variables are now defined as the average rates of all quarters from exit quarter to measure quarter. This is a change from the previous models that were aligned to just the exit quarter. The sub sections below explain why this decision was made and the evidence for this decision.

Background and motivation

Since the original implementation of the WIA models the economic condition variables (i.e., unemployment rate and the industry share variables) have been aligned with the exit quarter of the cohort. Over the years there have been considerations to reassess the decision to align timing of this data to the timing of participant data to better measure the impact that economic changes have on participant outcomes. However, changes were not made in previous iterations of the models (i.e., the PY 2020-2021 models) given the minimal amount of WIOA data available at that time. Previously there was not sufficient data to properly assess which alignment would result in a better performing model. For this iteration there was sufficient WIOA data available for these program years as well as more variance in the economic data to pursue potential changes in this version of the models.

In addition, ETA has been assessing how to improve the performance of the models in light of the impacts resulting from the COVID-19 pandemic. In those efforts, various other data sources were considered as inputs to the models, however, no measure was found that satisfactorily fit the current implementation of the models. Potential measures of past and future economic shocks were incompatible with the current models (e.g., there was not sufficient past data, there was no certainty in the availability of future data, the data did not align as needed geographically, etc.). One aspect where changes could be made to the models, however, was in the alignment of the economic data to make the models more responsive to economic changes.

Alignments considered

Over 12 different potential combinations of alignments were tested across all models. The list of potential alignments was reduced to three options after the initial analysis in order to adhere to the Department of Labor’s (along with Department of Education) overall philosophy of keeping the models as consistent as possible across programs and indicators. ETA did not want to use different alignments for different models unless there was a strong justification for doing so.

The final three alignment options tested were:

  • Exit Quarter - the past alignment where the economic data is aligned to the exit quarter of the cohort
  • Measurement Quarter - aligning the economic data to the measurement quarter of the cohort (e.g., for ERQ2 and MEQ2 the 2nd quarter after the exit quarter, for ERQ4 the 4th quarter after the exit quarter, etc.)
  • Average from Exit to Measure Quarter - aligning the economic data to the average of all quarters from the exit to measurement quarter of the cohort (e.g., for an ERQ2 observation this would be an average of 3 quarters with the exit quarter, 1st quarter after exit, and the measurement quarter)

Relationship to Outcomes

The plots below show the general relationship that selected economic condition variables have with the ERQ2, ERQ4, and MEQ2 outcomes in the prepared modeling data. Overall, the plots show that the relationship is the same direction across the different alignment types with some differences in the magnitude for the different economic condition variables. For some of the variables (e.g., the manufacturing industry share, etc.) the relationship is almost exactly the same for each alignment type, however, there is variance for other economic condition variables (e.g., the unemployment rate, leisure industry share, etc.).

Unemployment Rate

Leisure

Financial

Construction

Manufacturing

Model Prediction Estimates

The plots and tables below show tests of the models using the final different economic condition alignments. The different specified models were fit using a subset of training data and they were used to predict a subset of test data. For each WIOA program, the top 3 plots show the results of the three different models for the ERQ2 indicator and the bottom 3 plots show the results for the ERQ4 indicator. In almost all cases, the models that use the average alignment were the best performing models with a higher r-squared and lower root-mean-square error (RMSE).

In addition to how well the various models predicted performance, other model performance estimates were examined such as the direction and magnitude of the coefficients for the economic condition variables using the different alignments. Across all analyses, the models using economic conditions aligned to the measurement quarter and the average from exit to measurement quarter outperformed the alignment to the exit quarter. There was not a large difference between the alignment to the measurement quarter and the average from exit to measurement quarter, however, in most instances the average alignment was slightly better.

Adult

Dislocated Worker

Youth

Wagner-Peyser

Improvements to MSG Observations

This section describes the changes in how group observations are created and used in the MSG models.

Background

The MSG indicator is calculated differently than the other indicators, which are exit based. MSG is calculated as the rate of participants receiving at least one skill gain in a program year of those who are in training/education during the time period. If a discreet quarter is used for MSG observations, like what is used for the exit-based indicators (i.e., ERQ2, MEQ2, ERQ4, CRED), then the rates for those quarterly observations would be significantly lower than the typical annual rate and this would not be a good modeling approach. A simple solution to this problem could be to use annual observations (i.e., State-Program-Indicator-PY observations), however, that would drastically reduce the total number of observations which would have other negative impacts on the performance of the models.

To handle this issue, the PY 2020-2021 Statistical Adjustment Models used rolling-4 quarter observations (see the MSG section of the PY2020-2021 Model Selection paper for more details). This approach keeps the higher number of observations (i.e., quarterly) while also resulting in MSG rates (i.e., the predicted target for these models) that are in line with annual rates. The downside of this approach is that it results in observations that are not truly independent—participants can be, and typically are, in more than one observation group. This impacts both the resulting estimates of any model as well as the ability to test and select the best performing model since the training and testing data sets are not truly independent. The overarching resulting negative consequence is that the resulting models tend to overfit the past data and do not perform as well as expected on new, unseen data.

Change in approach

A new method of using random sample groups was used for the PY 2022-2023 MSG models. This approach involves creating random group observations for each State-Program-Indicator-PY. Functionally, this works by creating multiple independent annual State-Program observations for the MSG models. Thus, the observations maintain the annual alignment, but the total number of observations used to fit the model and get the model estimates is increased.

For some states in some programs the total number of participants in the MSG denominator can be low in a particular PY. As a result, while the maximum number of random groupings created is set, not all State-Program-Indicator-PY groups have the set level of groupings. Instead, in cases of low number of participants, the random groups are pooled together so that every random group contains at a minimum 20 participants. This reduces the potential for inflating the variance in individual observations.

Multiple thresholds for the number of random groupings were tested and the number with optimal model performance was 6 random groups for each State-Program-Indicator-PY. This is the observation grouping used for the PY 2022-2023 MSG models.

Impact on MSG model performance

The plots below show an example of how the use of the random sample groups impacted the model performance and how 6 was determined to be the optimal number of random groups for this version of the models. The example model results below are for the Adult program using a simplified MSG model (i.e., the full specifications of the MSG models are not shown).

The first plot below shows the resulting values of the coefficients and their corresponding uncertainties for models fit with different observations. The models shown are those fit with Rolling-4 (i.e., the previous method used) observations and several numbers of random group observations (4, 6, and 8 random groups). As the plot shows, the relative direction and magnitude or the coefficients for the different variables are fairly consistent using different observation types, however, the biggest difference is the size of the error bars. Using random observations reduces the uncertainty since it results in more variance in the observations and increases the total number of conversations available (the random 4 type has the same number of observations as the rolling type, however, both the random 6 and random 8 have 1.5-2 times more observations).

Coefficient estimates using different observation group types

The plots below show an example of other tests that were done in determining the optimal grouping of observations. Each type of observation group type was fit with a subset of the total data (i.e., the “training” data) and then used to predict another subset of the data (i.e., the “test” data). The plots show that the random groupings predicted as well as the rolling groupings even though the the rolling groupings approach is certainly overfitting the data in this example (see above about the issue of overfitting using rolling groups). Also, the plots have a table showing some estimates of model performance. The using of 6 random groups was the optimal performing model with both the highest r-squared and lowest root-mean-square error (RMSE).

Model predictions and performance by observation group type

Other Model Changes

There were a number of relatively minor changes made to this version of the models as well. Descriptions of those changes are below.

WIOA data used

This version of the models was developed and fit with WIOA data reported for PY 2018-2020. The previous version of the models included data from PY 2017. The decision to not include PY 2017 data was due to changes made in the collection and reporting of data during that PY as well as some data quality concerns with the PY 2017 data. Also, due to data lags, only MSG had a full four quarters of data in PY 2017 while Q4ER and CRED did not have any outcomes in PY 2017.

Change in the measure of days in program

Previous models included a Median Days in Program variable which measured the median days a participant was in the program for the cohort. The intention of this variable is to account for the difference in participants who only engage with the programs for short light-touch types of services versus those who are seeking more long-term services. However, that definition of the variable presented a few issues. First, the measurement of the variable could vary widely for state observations if there was a significant mix in the types of participants served (e.g., in some cases the variable could have a range from zero to somewhere in the hundreds). Second, there was concern that the variable may be capturing some service-related aspects that are in the control of states. In addition, the variable was out-of-scale compared to other variables included in the models which made it sensitive to a large variance in the possible range of resulting adjustments for that variable—as defined that variable could range from 0-400 whereas other variables have a maximum range of 0-1 (i.e., they are calculated as a percentage of the participants in the cohort). Also, since it was out-of-scale, it was difficult to interpret the relative importance of the variable compared to other model variables.

To account for these issues, this variable was changed to Percent with More than 1 Day in Program. This new definition is more stable, better aligns with the intention of the variable, and is at the same scale as other variables in the model. In addition, it was found to be more statistically significant and predictive of participant outcomes across all models.

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 Employment Rate 4th Quarter after Exit Median Earnings 2nd Quarter after Exit Credential Attainment Measurable Skill Gains
female -0.1164554 -0.0194016 -1297.0538710 -0.0754982 -0.1616630
age2544 0.0195832 -0.1288478 417.9959121 0.1826835 -0.0800956
age4554 0.1883221 -0.2913959 -2087.0759462 0.1897626 -0.1625537
age5559 -0.1608932 -0.6459384 540.9828310 0.2852227 -0.4177665
age60 -0.5644019 -0.4948326 -7312.3854301 -0.0960763 -0.5329271
hispanic 0.1026530 -0.0879317 -2450.8816819 0.0069002 -0.1563501
raceasian 0.1814753 -0.2060114 -1202.0930081 0.0381062 0.0434617
raceblack 0.0115136 -0.1379268 -2487.3449946 -0.0231444 -0.1943251
racehpi -0.8625263 -0.3114198 600.7171759 0.2025170 0.1848883
raceai 0.1109147 -0.1776726 925.5437528 0.0536498 -0.1620835
racemulti -0.0435352 0.1421427 1786.1417339 0.2495502 0.0809061
hsgrad 0.0737488 0.0725901 220.3100280 0.1867228 0.3201319
collegedropout -0.0521057 0.0613083 -973.7394240 0.1214942 0.3710591
certotherps -0.3552833 0.1354454 1170.6797235 0.2349459 0.1204885
associate 0.2738871 0.2255826 1332.5752938 0.0884570 0.2762699
ba 0.0983109 0.2066026 6553.8279457 0.1357223 0.4706072
gradschool 0.1487756 0.2519462 2701.2038546 -0.7819611 1.0229153
empentry 0.0896806 0.0193005 1230.1963837 0.0256484 0.1405065
edstatentry 0.1210834 0.0287306 3419.0385125 0.1405279 0.0139608
disabled -0.2081486 -0.0652060 -2793.5987386 -0.0166434 -0.3448531
veteran -0.3623052 -0.3548302 1373.5871539 0.0743298 0.3438935
englearner -0.2961880 0.0164404 -1151.7181311 0.0533740 0.3340221
singleparent 0.1950558 0.0095720 2287.4012583 -0.0704553 0.0441298
lowinc -0.0398202 -0.0262880 5.8330118 -0.0094366 -0.1185147
homeless -0.0696183 -0.1566199 -1146.1920343 -0.3807509
offender -0.1191532 -0.1158266 2209.0540203 0.1333606 -0.1521313
dishomemaker -0.4256214 -0.1049708 -4781.8103944 -0.1188503
recwages2qprior 0.2680227 0.1228531 1796.8739615 -0.0754767 -0.0363397
longtermunemp 0.0604106 0.0172354 -1228.0227011 0.1180049 0.0584329
uiclaimant -0.0043726 0.0426163 -428.5231384 -0.0194123 0.0986083
uiexhaustee 0.0645247 0.5619567 -2691.8031578 -0.1883773
recotherasst 0.0519502 0.0349086 -658.4234584 -0.2849648 -0.1950968
recssi -0.2224490 -0.1746911 1730.4896237 0.0934545 0.4656446
rectanf -0.1617803 -0.1714209 -4085.3143709 -0.2344063 0.1690786
daysinprog_over1 0.1423589 0.0540915 2092.2363327 1.2664194
natresources -3.5828610 -2.2270869 71514.9331277 0.0743554 -8.2147750
construction 0.4978733 0.0773361 45888.5923412 1.7758941 -5.6564291
manufacturing -1.6760473 -3.7793675 -50584.4382167 10.3016317 -3.8947351
information -4.3771704 -10.6035847 -127934.7190210 -1.9904122 20.2496441
financial -4.3740136 -7.2091472 -27480.3604637 5.0816079 26.8702152
business -1.8276461 -4.4265584 -36313.0883537 7.4562283 -2.6018639
edhealthcare -1.8037498 -3.3977976 -32119.7537201 1.5762869 -2.5835919
leisure -2.6022270 -2.3666778 -22488.2694488 4.0539844 -4.2956463
otheremp -1.8292459 -7.8943503 -90961.7792208 7.4646200 -17.5521126
publicadmin -3.0904832 0.7594137 61252.7890364 3.9904104 -0.8166200
ur -1.7900147 -2.2993763 -6692.7609547 0.7558291 -1.7366917
AK 2.3610355 3.2501171 12227.6111240 -3.7900980 2.1781040
AL 2.1535080 3.6042531 22121.4567574 -4.4949171 1.9912659
AR 2.1397429 3.6396402 22412.2134180 -4.4286777 2.1381872
AZ 2.2624700 3.7947940 22128.6043422 -4.2579003 0.9016624
CA 2.2961864 3.9017054 25205.3197092 -4.3106882 1.5536221
CO 2.3034134 3.8423867 21936.5742716 -4.1187982 1.2817868
CT 2.2892286 4.1055254 28226.6990534 -4.4580782 1.0938481
DC 2.8178472 3.9911498 19418.3066094 -5.2440595 2.6983720
DE 2.2412387 3.9992413 24379.8783561 -4.3442999 -0.2369838
FL 2.2467667 3.7514617 21467.1412017 -4.1066622 1.2510486
GA 2.2504674 3.8271085 23863.7700731 -4.3539588 1.1580227
HI 2.5436566 3.6335240 18463.6071064 -4.3344227 1.6588955
IA 2.2826105 3.8074266 23942.8099778 -4.5755314 1.1355176
ID 2.2630215 3.5541453 17464.0610890 -4.2323269 2.0142331
IL 2.2131118 3.9391396 27310.9717914 -4.3300604 1.4054024
IN 2.1879879 3.7598639 25318.1963508 -4.8738127 2.2223140
KS 2.2614636 3.7495421 22695.0672492 -4.4454960 1.6286822
KY 2.0848858 3.5607448 23735.7849825 -4.5980499 1.4524916
LA 2.0926476 3.4913974 18950.7521871 -4.0293280 2.1966364
MA 2.2848299 4.1235763 29575.2848639 -4.2475816 1.0565872
MD 2.2971497 3.6984470 20062.7547974 -4.3023032 1.8373108
ME 2.1642560 3.6330387 22165.9557454 -4.2324832 1.6499192
MI 2.2229700 3.9130293 27005.3788162 -4.6592558 1.8687042
MN 2.2564723 3.8416569 25651.3315838 -4.3961388 1.3132644
MO 2.2169871 3.8045917 24679.0103981 -4.4253396 1.3282041
MS 2.2338979 3.6293006 22342.0048126 -4.3500849 2.0119746
MT 2.1459436 3.3510788 15574.2714346 -3.7642542 2.3104896
NC 2.1883334 3.7785867 22502.9127566 -4.6434245 1.4886922
ND 2.2095276 3.4306061 14805.6509010 -3.7606189 1.8145016
NE 2.2268274 3.7703790 21379.9624197 -4.4664290 1.1466155
NH 2.2746940 3.7762664 25880.3205582 -4.1464421 1.7748863
NJ 2.0634915 3.7020540 24579.6499143 -4.2228531 1.1915366
NM 2.2234400 3.5087710 16091.4561899 -4.0363457 2.2947766
NV 2.3266254 3.5330507 19089.9816515 -4.1944696 2.2930522
NY 2.3523004 4.0358558 26514.8891269 -4.2320652 0.5201472
OH 2.3126235 3.9727398 26604.2383027 -4.6190867 1.7233952
OK 2.2069144 3.4542240 16418.9584622 -4.2008038 1.9918332
OR 2.1897205 3.7336610 22510.2763003 -4.3976232 2.0392513
PA 2.2508971 3.9136294 25033.8267524 -4.4110413 1.5355422
PR 2.3829210 3.2493822 15596.5141662 -4.8108519 1.7352421
RI 2.4353967 3.9726411 25417.5275801 -4.3857586 1.3016747
SC 2.1797550 3.7210983 22989.4765741 -4.7313260 1.8477890
SD 2.2464020 3.6280281 18623.5431033 -4.2560782 1.4735613
TN 2.2051790 3.7394543 23823.1141207 -4.5723297 1.6980977
TX 2.1898036 3.7384642 21890.6281809 -3.9927369 1.6463976
UT 2.2308710 3.6988400 21799.8197321 -4.3580325 1.1402722
VA 2.2557302 3.8822476 22674.9306189 -4.5016769 1.8388298
VT 2.2496134 3.5015012 21340.6540845 -4.1013034 2.1288615
WA 2.2341224 3.8297333 24005.1021006 -3.9774767 1.1469909
WI 2.1716267 3.8330670 24943.2789498 -4.8438364 1.6363799
WV 2.1985983 3.3967417 16829.2656305 -3.8749762 1.9430147
WY 2.4131424 3.2497199 8324.2529282 -3.5247417 2.6743057
wages2qprior 0.1775896
daysenrolled -0.0002357
daysenrolled_under30 -0.4424028

Dislocated Worker

Term Employment Rate 2nd Quarter after Exit Employment Rate 4th Quarter after Exit Median Earnings 2nd Quarter after Exit Credential Attainment Measurable Skill Gains
female 0.0079473 -0.1158491 -2003.914530 -0.1523157 0.0643253
age2544 0.0018045 -0.1470789 -2743.851567 0.1318364 0.0060426
age4554 -0.0745152 -0.3554356 -4619.722096 0.3677512 0.0516725
age5559 -0.1051450 -0.2305731 -6255.894946 0.2400454 -0.1867318
age60 -0.0628928 -0.0884389 -3495.603950 0.0750588 -0.3572734
hispanic -0.0498620 0.1458529 1426.273993 0.0650629 0.0331318
raceasian 0.3829655 0.3048815 -3317.048096 -0.5722478 -0.1244146
raceblack -0.0065056 -0.0952011 -2693.408686 0.1635878 -0.0304015
racehpi -0.6032889 0.1686873 -5032.777533 0.3954773 0.5990021
raceai -0.3469431 -0.0903704 -4074.632269 -0.3462610 0.0723498
racemulti 0.2245967 0.0934201 -4118.279379 0.4396034 -0.0763649
hsgrad 0.0479166 0.0256283 991.164315 -0.0104061 0.0859111
collegedropout 0.0624857 0.0311095 1574.359766 -0.0517120 -0.0945785
certotherps -0.0975038 -0.0048805 -1329.569453 -0.0116239 0.1494804
associate -0.0243417 -0.0085395 -486.261570 0.3183844 0.2307132
ba 0.0241357 0.1330652 5283.699062 -0.1385302 -0.0697969
gradschool 0.1019207 -0.0324463 3662.150183 -0.2679934 0.0873482
empentry 0.0710393 -0.0151385 2636.651051 0.1787368 0.0209414
edstatentry -0.0156740 0.0920288 1266.307863 0.1223546 0.0653426
disabled -0.0359762 0.0668849 -723.114182 -0.4388505 -0.1542176
veteran -0.3229511 -0.3454459 1353.763761 -0.0631564 0.1505450
englearner -0.2084514 -0.0714989 2561.442252 0.1431732 0.0020623
singleparent 0.0957543 -0.0171231 2109.289835 -0.2467914 0.0845394
lowinc -0.0439964 -0.0219919 73.946709 0.0878768 -0.2531673
homeless 0.4362098 0.0859109 3087.940145 -0.2093717
offender -0.2135710 -0.0690994 574.703472 -0.0781884 0.2017587
dishomemaker -0.5536832 0.0658195 -3686.700229 -0.2298289
recwages2qprior -0.0250565 -0.0203835 -190.545564 -0.1400836 0.0671882
longtermunemp -0.0342940 -0.0016496 1348.790864 -0.1588026 0.2811631
uiclaimant 0.0747154 0.0200682 411.157154 -0.0689975 0.1519805
uiexhaustee 0.0051349 -0.1140699 -814.927137 -0.1752782
recotherasst 0.2951304 -0.1051718 3137.813883 -0.1092596 0.2495132
recssi 0.5141563 0.1706456 -1595.891653 -0.1954231 -0.6643414
rectanf 0.5099769 0.1821993 2074.051652 -0.1232067 0.1795380
daysinprog_over1 0.1412972 0.1602730 883.912823 3.2149380
natresources 2.4001854 9.5543614 58881.652956 4.3678539 -6.5069145
construction 0.7397366 0.2696532 43095.557027 -1.7009277 -17.3565869
manufacturing -2.0429272 0.5885063 -3643.641764 3.5473036 -10.0742575
information -0.3584514 -9.7191999 56629.946023 -22.6585927 -6.6747408
financial -0.8798234 1.5914459 46858.128956 7.0385443 26.9420881
business -1.4508697 -4.0395373 42893.291508 6.8386779 -5.2222533
edhealthcare -0.9847797 0.6818432 -32098.306546 0.5861858 -10.8796277
leisure -2.5585120 1.8971188 -44082.629315 3.8135317 -5.6884624
otheremp -1.7054928 5.4715833 -35961.777750 -0.8859140 -22.8569197
publicadmin -1.1943306 5.0137871 -93000.430143 13.1356290 1.9830830
ur -2.5065805 -1.0767055 -10161.698833 0.6448400 -0.6895110
AK 1.7817254 -0.2722982 24455.893887 -5.6893395 5.5403423
AL 1.8300814 0.5186350 16446.022994 -5.2451144 5.6471708
AR 1.8397198 0.6404762 13994.682026 -4.8051062 6.0721175
AZ 1.8422971 0.5873597 10624.295523 -5.0019723 4.6203620
CA 1.6981122 0.5661894 10933.285905 -4.6000249 5.7728366
CO 1.7346095 0.6917692 10440.870481 -4.7139453 5.3311123
CT 1.8456119 0.6876063 14105.584259 -4.7305174 5.4125096
DC 2.0154604 -0.0180737 31897.996129 -7.6409672 5.7233862
DE 1.7484833 0.7031652 11025.001419 -5.1253840 3.6010169
FL 1.8753332 0.7160537 12694.216929 -4.9281296 4.9495266
GA 1.8464297 0.9634073 13644.420619 -4.8694919 5.0873620
HI 1.7103278 0.0951917 24416.306429 -5.0752718 5.4519504
IA 1.7480644 0.3958455 13828.061151 -4.9412218 5.1960903
ID 1.6874027 0.2752244 11847.320151 -4.9109374 5.7699003
IL 1.8556970 0.8377546 13763.690008 -4.9296048 5.1040514
IN 1.8592352 0.5472187 14315.490137 -4.9072947 6.3132013
KS 1.8227619 0.5542232 14674.402606 -4.8376949 5.6287274
KY 1.8318450 0.4874513 13510.383830 -4.9697603 5.6442573
LA 1.6100685 0.3420423 14796.997574 -4.8544792 6.2674371
MA 1.7605029 0.8890074 13045.546878 -4.5292809 5.6297998
MD 1.8194947 0.7019381 17439.974853 -5.5765977 5.6684692
ME 1.7760105 0.4921161 15142.735092 -4.7172422 5.8754548
MI 1.9759352 0.8436754 13186.627764 -4.9003361 5.9156608
MN 1.8218655 0.6484603 14669.622232 -4.6967444 5.6997620
MO 1.8029367 0.7429034 13804.855178 -4.8984050 5.5304170
MS 1.8288502 0.4297694 18354.361602 -5.1493084 6.0032798
MT 1.5638678 -0.0172635 18016.667678 -5.1130436 5.7830012
NC 1.7832076 0.6605863 13605.932381 -5.2454579 5.5613083
ND 1.5753070 0.0240112 12367.573330 -4.6613485 5.7322420
NE 1.8313845 0.6086865 12321.361126 -5.0273756 5.2662588
NH 1.8074577 0.6825057 15369.145766 -4.4819539 5.8279640
NJ 1.5923633 0.6955967 12743.592474 -4.9547554 4.8818624
NM 1.6526432 0.0648132 15143.370634 -5.3150024 6.1056962
NV 1.9945757 0.4690931 17061.229200 -5.1296453 5.5276715
NY 1.6081929 0.5830365 13193.838320 -4.9312996 5.0059511
OH 1.9634433 0.7856543 14496.369169 -4.8466227 5.8220566
OK 1.6695575 0.1476808 14725.446725 -5.1285479 5.5565512
OR 1.7142720 0.3421626 10922.875563 -4.6825459 6.1312963
PA 1.8527847 0.7065716 14014.882727 -4.8063103 5.6613012
PR 1.6265641 0.0417858 18202.228130 -6.7843056 4.1960859
RI 1.9555465 0.6417923 16055.419164 -5.0846035 5.3414247
SC 1.8657637 0.7106004 15494.137260 -5.2694156 5.6219137
SD 1.7086633 0.1698174 16151.007084 -4.9949641 5.4528249
TN 1.8866312 0.7668737 13772.097803 -5.0304271 5.5093368
TX 1.6495551 0.5838741 10032.823053 -4.7438480 5.4076981
UT 1.7644680 0.8175546 10929.464737 -4.6259800 5.3625783
VA 1.8860088 0.9687286 13024.281716 -5.2830197 5.6615113
VT 1.7913809 0.2518623 16811.898521 -4.6639611 6.4599632
WA 1.6181367 0.5736714 10775.411575 -4.1149515 5.7903786
WI 1.8788402 0.5795538 14358.807096 -4.8776997 5.9082792
WV 1.6612774 0.1562318 17067.015744 -4.8099848 5.9601064
WY 1.5556423 -0.5115134 16307.618982 -5.1613829 6.1806320
wages2qprior 0.362915
daysenrolled -0.0003070
daysenrolled_under30 -0.3491565

Youth

Term Employment Rate 2nd Quarter after Exit Employment Rate 4th Quarter after Exit Median Earnings 2nd Quarter after Exit Credential Attainment Measurable Skill Gains
female 0.0265358 0.0654541 -479.8577868 0.0709051 0.0665433
age1415 0.1309946 0.1384315 -2427.8446737 -0.3258193 0.0136530
age1617 -0.0511380 0.1280872 -2477.5207706 0.0416779 -0.1413876
age1819 0.0348676 0.0396806 -3145.8117754 0.0145013 0.0327269
age2021 -0.0081631 0.0367675 158.4882012 -0.1726319 0.0308371
hispanic -0.1297335 -0.0050766 -490.2115261 0.1348938 -0.0803035
raceasian -0.0278930 0.0981696 1320.1603428 -0.0279369 0.0654631
raceblack -0.0800103 -0.0705729 -2125.1932863 0.0331021 0.1032926
racehpi -0.2075708 0.2088509 -292.7229517 0.2110207 -0.0095982
raceai -0.4506121 0.0815158 -2035.2920482 0.1455095 -0.0937753
racemulti 0.0451947 0.0729209 2613.3872235 -0.0706968 -0.2112820
hsgrad 0.1938443 0.1498486 832.6305394 0.1460458 0.0011401
collegedropout -0.3544344 0.1839287 -2506.9333458 0.4421587 -0.2023788
certotherps -0.3194394 -0.3500087 -2739.7906687 -0.4358159 -0.1934203
associateorba 0.6207889 -0.3814620 1178.8104254 -1.3561968 -0.3419240
empentry 0.2268432 0.0936093 799.7668394 0.0046441 0.1436742
edstatentry 0.1686054 0.0490839 440.9543367 0.0461745 0.1182912
disabled -0.1861237 -0.1336718 -521.6470985 0.0648804
englearner -0.0104578 -0.1772296 1073.6773292 0.1003560
lowinc -0.0178521 0.0074753 533.4785635 0.0086434
homeless 0.1620340 -0.0737007 -863.2404101 0.2339531
offender 0.0001582 -0.1087546 696.2349175 -0.1058868
yparent 0.0258201 0.0318090 -1234.6504108 0.0524036 -0.0157460
basiclitdeficient 0.0947207 0.0140949 117.0359768 -0.0328235 -0.0103818
yfoster -0.2566638 -0.0545146 -1315.2928094 -0.2504521
longtermunemp -0.0194232 0.0202028 1025.3729815 -0.0306355 0.2501545
uiclaimant 0.1157840 -0.0696179 155.9486379 -0.0517237
recotherasst -0.0097569 -0.1638307 -1003.2409726 -0.2399670 0.4005072
rectanf 0.0813618 -0.0014602 489.7983906 -0.4006139 -0.3933046
recssi 0.0170244 0.0070827 -3130.0430913 0.2258376 0.0828034
ynaa -0.0341394 -0.0439177 -374.7889386 0.0361302 -0.1404093
daysinprog_over1 -0.0472776 0.4315042 1659.7275594 1.2644214
natresources -4.1019483 3.9394870 -759.5897471 -8.9909535 -5.7974862
construction 1.7489552 2.0774805 28178.9291248 6.9224415 -7.7597269
manufacturing -2.4824030 -2.4243287 -1764.5245781 0.8878235 -11.0363047
information -12.8134871 -14.1770419 -8732.1640409 -18.3982577 6.1665629
financial -8.3184877 -4.9068529 3907.1896714 -5.9209466 20.0776842
business -4.3565642 -4.5171454 17051.3461259 2.4476592 -4.3184858
edhealthcare -3.9362594 -1.4946893 -9956.6166044 -0.6806486 -7.2531426
leisure -1.8895682 1.8863320 7512.8882648 1.6281511 -1.4574945
otheremp -2.8090943 -1.9157711 -44886.9930710 -13.8746607 -17.0802967
publicadmin -1.2923932 6.7350506 81354.7846141 -0.7137016 7.6523366
ur -0.6687081 -0.6026955 -1479.6748159 -0.5164137 -0.6567638
AK 3.2655929 -0.0083840 -7025.3673354 -0.0458526 2.5905460
AL 3.2926104 1.0789956 -2555.7935804 -0.7107478 3.5392265
AR 3.3374998 1.2615007 -685.2178977 -0.5223443 3.9867226
AZ 3.5866332 1.2682171 -1993.9096488 -0.3968308 2.6391290
CA 3.6760518 1.3911513 -1708.2011648 -0.0529022 3.4904014
CO 3.5929571 1.3161435 -2709.6425955 -0.2115509 2.9624975
CT 3.7632412 1.6798939 1048.8164406 0.1129576 3.5634010
DC 3.6380508 0.2193937 -15174.6007436 0.7677467 1.6421460
DE 3.7185003 1.6242573 -1040.1176221 -0.1039816 2.1693277
FL 3.4974864 1.2355020 -2250.4030160 -0.2384955 2.7011095
GA 3.5553625 1.5318440 -1829.2871885 -0.2181712 2.9615408
HI 3.3729513 0.3167478 -4019.7173759 -0.6990709 2.8816735
IA 3.4534606 1.2948192 208.0761680 -0.2974974 3.4330733
ID 3.3535570 1.0139806 -2597.1556216 -0.4885434 3.5301297
IL 3.5961438 1.6363091 195.4976886 -0.0980185 3.1053836
IN 3.3390484 1.3648507 -364.2224968 -0.4992279 4.4826210
KS 3.4924623 1.2796677 -2062.4970166 -0.3632650 3.5972522
KY 3.1440167 1.0896463 -1686.1458936 -0.4892686 3.5427190
LA 3.1939887 0.9007747 -1767.3414208 -0.6320749 3.4464291
MA 3.8673759 1.7006881 -54.7305358 -0.0877656 3.3252050
MD 3.4506347 1.0720973 -4943.6620093 -0.4519384 2.8642530
ME 3.3246382 1.2187088 -621.4845048 -0.4401122 3.6503744
MI 3.5512795 1.5889224 432.0108868 -0.2630061 3.8538584
MN 3.6126565 1.4784895 364.2742636 -0.1602566 3.3676277
MO 3.5430822 1.4466271 -491.5624268 -0.3774967 3.2907032
MS 3.3580471 1.1038328 -854.2853275 -0.3625602 3.9600161
MT 3.2382894 0.3738653 -3446.0802046 -0.4826839 2.8244342
NC 3.4356852 1.2939384 -2215.9346732 -0.5562142 3.2529106
ND 3.3903848 0.7359041 -278.9283592 0.0166135 3.2409749
NE 3.5082300 1.3512978 -1321.0230431 -0.4409524 3.0124386
NH 3.4244525 1.4077172 43.2998544 -0.2034574 3.8047079
NJ 3.5115184 1.3878865 -1733.8332548 -0.3108952 3.2173538
NM 3.3932771 0.6194430 -3706.0109047 -0.4753738 3.2409123
NV 3.1159277 0.6306552 -3188.8882025 -0.9573076 2.7462533
NY 3.8427032 1.5737970 -889.0620003 0.2676858 2.5611598
OH 3.5255071 1.5680825 -113.3165782 -0.3310445 4.0271715
OK 3.3809146 0.8090255 -3618.3068999 -0.3577391 3.3324705
OR 3.3737466 1.1133250 60.6244178 -0.1805638 3.9830533
PA 3.5166981 1.4129643 -175.5009936 -0.2072776 3.8122511
PR 3.3280826 0.4197376 -11567.5866410 -0.8421713 1.7340781
RI 3.7046336 1.3595590 -1130.5430248 -0.2091282 3.2623539
SC 3.3575492 1.2454350 -1884.0817075 -0.5303200 3.5678688
SD 3.4426612 0.9638394 -1941.3581837 -0.3554793 3.0391734
TN 3.3833771 1.3729529 -859.0263165 -0.4753624 3.5474379
TX 3.5066924 1.3325184 -571.4251812 -0.3828534 3.1392618
UT 3.5692661 1.3535058 -2739.2968378 -0.3952718 3.1533458
VA 3.5377518 1.4186005 -3776.7518520 -0.4045090 3.2773562
VT 3.3698338 1.0202762 -1713.2700473 -0.4683207 3.8493542
WA 3.6076805 1.4047115 -902.8604330 0.3424422 3.2229202
WI 3.4533804 1.4131042 -169.8768084 -0.3784713 3.8988932
WV 3.2112357 0.6738901 -3040.4047685 -0.2140772 3.6221900
WY 3.2279687 0.0033678 -4669.0170317 0.0910964 3.3671172
wages2qprior 0.0981894
daysenrolled -0.0002303
daysenrolled_under30 -0.0504292

Wagner-Peyser

Term Employment Rate 2nd Quarter after Exit Employment Rate 4th Quarter after Exit Median Earnings 2nd Quarter after Exit
female 0.0123569 -0.0156906 -1636.8148129
age2544 -0.5470337 -0.0908819 -3854.1133079
age4554 -0.8324689 -0.3960175 -7244.8966498
age5559 -0.7383471 0.3041169 -5507.8189575
age60 -0.8328485 -0.3947670 -3958.4214217
hispanic 0.0665073 0.4007507 -62.3130950
raceasian -0.1742388 0.0748339 -785.4883308
raceblack -0.0446409 0.0324483 -3867.2796291
racehpi -0.7267985 -1.1783048 7376.0162578
raceai -0.3897629 -0.5803073 670.3052820
racemulti 0.0940043 0.2660877 1050.7222578
hsgrad 0.0773133 0.0142966 112.2973884
collegedropout 0.1564546 0.1643223 2206.4084181
certotherps -0.5158372 -0.2043862 -1551.2163721
associate -0.5255334 0.0465126 -3124.4249486
ba -0.1238619 -1.2298014 614.0883779
gradschool 0.0897600 2.0883415 484.1250724
empentry 0.1584317 0.1043918 -233.0788227
edstatentry -0.0683563 -0.0320909 586.1154151
disabled -0.0777220 0.0620101 3751.1377305
veteran 0.2295303 0.1097907 740.1797986
englearner 0.0267084 -0.2558475 749.2328996
singleparent 0.0667668 0.2823061 -516.2162629
lowinc -0.0153524 -0.2149536 -63.8546213
rectanf -0.6409323 1.4801511 -3981.8829016
homeless -0.4362181 -0.1989377 -5669.2612496
offender -0.0897740 -0.1143212 979.1098315
dishomemaker -0.1319854 -1.1170810 -12916.4879207
recwages2qprior 0.5528796 0.0660245 3544.2325700
longtermunemp 0.1931023 0.0158711 780.3843367
uiclaimant -0.0301711 0.0179421 -69.9187756
uiexhaustee -0.1370288 -0.1142752 -298.2125947
recotherasst 0.2710293 0.4390260 8055.2633338
recssi -0.4098139 0.1513237 -8924.3002171
daysinprog_over1 0.0014630 0.0329153 210.8105036
natresources 0.6130328 -1.7206214 22029.8303888
construction 0.7965774 2.4273447 7385.6116692
manufacturing -2.7323663 -2.8513332 1606.7800778
information -3.2159821 -1.2264333 6742.4515156
financial -3.3592443 -4.3524160 -13708.0183392
business -4.9405516 -6.1979805 28313.0177713
edhealthcare -1.2618588 -1.7391564 -26515.1068848
leisure -1.3278617 -0.4972648 -12415.1127872
otheremp 6.9082612 2.2873060 -24115.2901605
publicadmin -2.8018383 -1.3645701 -17204.2843340
ur -1.2725755 -1.5972962 -3559.8773979
AK 2.2685745 2.2845371 11767.8624024
AL 2.3959560 2.5304451 10642.7251186
AR 2.3469865 2.5074697 10058.4588333
AZ 2.5402455 2.5053762 9519.4797540
CA 2.4277067 2.4623829 9087.7631260
CO 2.4463710 2.4858668 8754.7567942
CT 2.4316562 2.6019853 12187.2098991
DC 2.6085673 2.7232139 12895.8317097
DE 2.5125970 2.6416830 12289.7729973
FL 2.4429177 2.4149449 10614.4010198
GA 2.5352743 2.6514480 10544.4180358
HI 2.3190837 2.3565604 7979.4346498
IA 2.3274180 2.4938776 11019.0152444
ID 2.3137025 2.4640568 8376.5776116
IL 2.4888085 2.6489357 9728.3470922
IN 2.4299181 2.6100417 10290.3026531
KS 2.4517004 2.5488033 10043.7226408
KY 2.1191688 2.4668910 9222.9077688
LA 2.2066496 2.2442800 11461.1775123
MA 2.5943084 2.6221603 10977.0836905
MD 2.4756673 2.5945459 10966.6870540
ME 2.3269213 2.5069108 11267.9439679
MI 2.4797594 2.7460973 9485.9245610
MN 2.5793833 2.7231778 10961.0016302
MO 2.4307581 2.6062981 10458.3691762
MS 2.3939658 2.5320724 13236.0001089
MT 2.1061099 2.1845215 11608.8932066
NC 2.5340864 2.6624287 10588.9597576
ND 2.0596343 2.2291003 10541.4812447
NE 2.3998797 2.5792744 9958.6024732
NH 2.3867991 2.4917617 10693.4459117
NJ 2.5052897 2.5506840 10630.7495335
NM 2.2920890 2.2971954 9857.4106165
NV 2.4157581 2.3120150 9603.9491042
NY 2.4259939 2.5537869 12483.4845251
OH 2.5817854 2.7259173 10620.5969331
OK 2.3395004 2.5262689 8726.1586981
OR 2.2619580 2.5880497 9639.0211624
PA 2.4564285 2.6394610 11266.5545271
PR 2.4736743 2.0583408 9910.7470550
RI 2.4473980 2.6310439 11868.2074981
SC 2.4535793 2.5453318 10764.8149585
SD 2.4028268 2.3955904 11650.9261332
TN 2.4379660 2.6266509 9914.3338792
TX 2.3134515 2.3807882 9154.3061287
UT 2.4349097 2.5710789 8569.0059447
VA 2.6165980 2.7978715 9276.7271786
VT 2.3047018 2.2802403 11622.6376453
WA 2.4108024 2.4258212 9116.9544857
WI 2.5002803 2.6617964 10588.1787821
WV 2.0796950 2.2391381 10383.7454267
WY 1.9624342 2.0269816 10030.5655920
wages2qprior 0.5261825

All Model Estimates

Final Predictions

The tables below show the predicted outcomes (Estimate0) in PY 2022 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 2020) and the most recent economic conditions data for states (i.e., time period 7/1/2020 - 6/30/2021).

Adult

State Employment Rate 2nd Quarter after Exit Employment Rate 4th Quarter after Exit Median Earnings 2nd Quarter after Exit Credential Attainment Measurable Skill Gains
AK 0.6901240 0.7208932 10419.546 0.7130243 0.5833553
AL 0.7688764 0.7316356 6364.424 0.7553580 0.6550748
AR 0.8055381 0.7845751 6649.098 0.8146881 0.7094469
AZ 0.6841254 0.6378890 6785.170 0.7800859 0.5921281
CA 0.6244913 0.5943265 6518.155 0.6620318 0.5009998
CO 0.7372274 0.6826593 7278.553 0.8061082 0.5959043
CT 0.6950767 0.6573754 6012.589 0.7288076 0.5981761
DC 0.6666859 0.6600635 7616.146 0.5197326 0.6562232
DE 0.7792663 0.7095297 6627.478 0.6684099 0.2140069
FL 0.8441431 0.8068283 8410.815 0.8074142 0.4439760
GA 0.8313461 0.8043522 7121.196 0.7558746 0.4230926
HI 0.6394902 0.6544714 5515.631 0.5234626 0.3054314
IA 0.7146066 0.6662165 6107.830 0.6487614 0.4253394
ID 0.8211350 0.7561448 7098.789 0.6956105 0.4610362
IL 0.7984648 0.7583447 7450.266 0.7710665 0.5423197
IN 0.7791278 0.7538480 6920.455 0.7132921 0.6318570
KS 0.7602702 0.7187442 6783.666 0.7645707 0.6405666
KY 0.7176404 0.7259014 6924.979 0.7080150 0.4906628
LA 0.6517961 0.6490634 6143.749 0.7427624 0.6296598
MA 0.6908593 0.6640518 6350.233 0.7359799 0.3926775
MD 0.7797218 0.7767184 7268.238 0.6618370 0.6392494
ME 0.6848111 0.6919661 5532.068 0.6952164 0.4829046
MI 0.8030358 0.7477182 7302.259 0.8175945 0.4961638
MN 0.7592282 0.7150718 7856.537 0.7843879 0.6225437
MO 0.7618957 0.7063311 7087.198 0.6813163 0.5467180
MS 0.8439755 0.8166100 6475.067 0.7158487 0.5173888
MT 0.7083139 0.6781100 6021.201 0.5672529 0.5670092
NC 0.7819169 0.7648183 7052.272 0.6241467 0.5217342
ND 0.7732208 0.7254635 7167.822 0.6533027 0.5927856
NE 0.7886313 0.7664673 6692.695 0.6458780 0.5905651
NH 0.7727143 0.6931791 6441.677 0.8486636 0.7201650
NJ 0.6100047 0.6081538 5684.578 0.6512610 0.5132380
NM 0.7730708 0.7439824 7785.230 0.5912123 0.6034205
NV 0.7022449 0.6294733 5848.588 0.7774989 0.5760586
NY 0.6284132 0.5854393 6305.000 0.5278464 0.3841218
OH 0.7930351 0.7614004 6713.411 0.7570251 0.6290061
OK 0.7156831 0.6834624 5509.763 0.7475850 0.5992498
OR 0.6550965 0.6641158 7018.569 0.6046189 0.4892417
PA 0.7184125 0.6772496 6159.923 0.7413577 0.5284646
PR 0.4970775 0.4856739 1912.367 0.4402422 0.8432250
RI 0.7829268 0.7543777 6266.278 0.7841223 0.5201595
SC 0.7549322 0.7366752 6192.744 0.6495743 0.5520608
SD 0.6718290 0.6901044 5442.662 0.5324522 0.6538071
TN 0.8404644 0.8177908 7024.547 0.7164003 0.6884800
TX 0.7034563 0.6733255 5439.427 0.7151360 0.6170938
UT 0.7538248 0.7130210 6970.111 0.7565950 0.4858333
VA 0.7856404 0.7913862 5928.077 0.7736057 0.6801463
VT 0.6356612 0.5625227 5379.375 0.8027506 0.5130433
WA 0.6126234 0.6238418 8213.490 0.6539390 0.2787910
WI 0.7270224 0.7118869 6099.466 0.7192966 0.4647295
WV 0.7127365 0.6912000 6144.259 0.8046295 0.3763820
WY 0.7418004 0.6714984 7123.792 0.7289674 0.7217686

Dislocated Worker

State Employment Rate 2nd Quarter after Exit Employment Rate 4th Quarter after Exit Median Earnings 2nd Quarter after Exit Credential Attainment Measurable Skill Gains
AK 0.8203544 0.8153904 11475.509 0.5757999 0.6127883
AL 0.7681167 0.7985020 8536.929 0.7898576 0.5420294
AR 0.8380821 0.8567497 7227.403 0.7905055 0.6773608
AZ 0.7695101 0.7124847 7925.933 0.7446721 0.5040832
CA 0.6800848 0.6809753 8766.870 0.6861230 0.4798292
CO 0.7421609 0.7364198 11104.974 0.6859822 0.5639422
CT 0.7787156 0.7457309 9662.076 0.6909235 0.4684334
DC 0.7671368 0.6988461 10934.605 0.3595487 0.7158530
DE 0.7545606 0.7551318 8849.625 0.7634476 0.1985396
FL 0.8394036 0.8016282 10093.417 0.8265095 0.4695677
GA 0.8351429 0.8422173 9558.232 0.7932822 0.3721442
HI 0.7625933 0.7666337 9779.295 0.5291844 0.4337118
IA 0.8383831 0.8325446 9161.305 0.6345294 0.4311665
ID 0.7755181 0.7865273 7887.548 0.7643774 0.3799741
IL 0.8220831 0.8187291 10858.201 0.7634866 0.5332819
IN 0.7744589 0.7701865 8096.278 0.7148076 0.6579424
KS 0.8064807 0.8021902 9665.039 0.8690078 0.5810217
KY 0.7589620 0.7423629 8899.266 0.6818217 0.6896421
LA 0.6365633 0.6636438 7370.683 0.7977307 0.6859381
MA 0.7546827 0.7300071 11273.883 0.7764741 0.4527912
MD 0.8405487 0.8114154 10733.760 0.5679089 0.6726342
ME 0.7948371 0.8043907 7740.541 0.7257958 0.4703010
MI 0.8563393 0.8461415 8693.799 0.8313183 0.5315472
MN 0.8417528 0.8274031 12744.822 0.8150960 0.6952217
MO 0.7704643 0.7665765 9577.863 0.7239098 0.5807304
MS 0.7355991 0.7368499 5763.249 0.7000175 0.5179554
MT 0.8107634 0.7170396 8417.196 0.5910050 0.5916952
NC 0.7066549 0.7239181 7603.753 0.6456442 0.5831128
ND 0.8331862 0.8312381 11414.544 0.6905243 0.8298762
NE 0.9017866 0.8653958 8490.401 0.6216106 0.7209926
NH 0.8120410 0.7824878 10203.234 0.9433193 0.6416119
NJ 0.6140372 0.6279193 8565.708 0.7122263 0.5308066
NM 0.6639558 0.6985124 7665.463 0.6327075 0.5604370
NV 0.7793134 0.7590762 8776.416 0.8142619 0.6230268
NY 0.6043742 0.6204798 7814.890 0.4784046 0.4518318
OH 0.7876139 0.7857097 8785.211 0.8228888 0.6329641
OK 0.7330617 0.6939888 8360.127 0.7612205 0.5986351
OR 0.6954594 0.6590366 7290.029 0.6162517 0.4703925
PA 0.7934470 0.7863696 8957.047 0.7440529 0.4578832
PR 0.4993948 0.5294979 2380.728 0.5143687 0.5799614
RI 0.8253475 0.8465837 9435.076 0.7524796 0.4273357
SC 0.7882023 0.8043931 7935.239 0.6560046 0.5712992
SD 0.7177362 0.7592359 7473.103 0.5782644 0.8007161
TN 0.8281763 0.8195179 7944.368 0.7059884 0.6120854
TX 0.7039118 0.7022849 9133.994 0.7457004 0.6367455
UT 0.8085127 0.8242353 10780.616 0.7493589 0.4450824
VA 0.8539671 0.8127251 9033.870 0.7827709 0.6181214
VT 0.7574331 0.6735439 9559.512 0.8623943 0.6489357
WA 0.6431021 0.6639968 9264.830 0.7519898 0.2605630
WI 0.8084629 0.8140388 8896.625 0.7248677 0.5568509
WV 0.7679402 0.7863510 9416.510 0.8380036 0.4252945
WY 0.8161268 0.7890795 10107.925 0.6949415 0.8224490

Youth

State Employment Rate 2nd Quarter after Exit Employment Rate 4th Quarter after Exit Median Earnings 2nd Quarter after Exit Credential Attainment Measurable Skill Gains
AK 0.5542304 0.5459643 3540.831 0.5672933 0.3668920
AL 0.6545300 0.6491014 3225.518 0.4050690 0.5042400
AR 0.7303133 0.7604675 3597.451 0.6268476 0.5827598
AZ 0.7086015 0.6722740 5043.867 0.6079723 0.5704567
CA 0.6768530 0.6571763 3869.577 0.6042545 0.5778975
CO 0.6595122 0.6481441 4261.818 0.6059285 0.5328928
CT 0.7215929 0.7246152 3799.651 0.7805281 0.6856667
DC 0.5802474 0.5623033 4500.811 0.5336794 0.3232826
DE 0.5961431 0.6678275 1637.252 0.7622902 0.6418384
FL 0.8140674 0.7756925 3864.259 0.8329754 0.4352405
GA 0.7501182 0.7850210 3148.414 0.6777952 0.3640299
HI 0.7087033 0.5887634 4607.835 0.3998089 0.1557341
IA 0.7504739 0.7222087 3904.717 0.5500732 0.4156799
ID 0.7380214 0.7860147 4351.509 0.5213690 0.3550826
IL 0.7641375 0.7369256 3936.860 0.6954938 0.4780036
IN 0.7902135 0.7906205 3331.444 0.6532915 0.6825519
KS 0.7227004 0.7325707 3049.595 0.6631548 0.4920916
KY 0.6469976 0.6551444 3718.703 0.5759720 0.4614839
LA 0.7039941 0.7029423 2980.218 0.4748258 0.4542970
MA 0.6501635 0.6202854 3333.128 0.6265670 0.3101518
MD 0.7560702 0.7279439 3938.158 0.6807630 0.5074976
ME 0.6625393 0.7040333 3901.536 0.5839200 0.4190669
MI 0.7859508 0.7568920 3664.469 0.6986174 0.4033628
MN 0.7281163 0.7383223 4253.385 0.6613176 0.4887955
MO 0.7689042 0.7431410 3714.811 0.5484829 0.4845349
MS 0.8017190 0.8006806 2902.684 0.7743648 0.5761159
MT 0.6342571 0.5474470 3111.347 0.2420435 0.2730914
NC 0.7074279 0.7030036 3243.600 0.5188336 0.4349449
ND 0.7844801 0.7381977 4512.799 0.4620534 0.5673815
NE 0.7873012 0.7973870 4142.701 0.4578183 0.4113948
NH 0.7983609 0.7900202 4670.173 0.7434799 0.7455787
NJ 0.6135568 0.5823694 2455.464 0.5338999 0.6695006
NM 0.5816534 0.6332418 3329.469 0.4534580 0.3841020
NV 0.6154361 0.6146924 3937.844 0.4800614 0.3856960
NY 0.5602979 0.5445101 3313.185 0.5498542 0.5170697
OH 0.7294842 0.7189511 3139.549 0.5270893 0.5253590
OK 0.7295988 0.6912510 3785.551 0.6129449 0.5853872
OR 0.6262570 0.5958517 4509.000 0.5607338 0.3591436
PA 0.6468047 0.6298077 3234.197 0.6359138 0.5856799
PR 0.5392040 0.3586069 1426.407 0.0795771 0.2009522
RI 0.7348526 0.7161630 3380.899 0.7225242 0.4116254
SC 0.7794649 0.7449040 3621.559 0.6410844 0.5275940
SD 0.6876128 0.7041693 3352.828 0.4872014 0.4715897
TN 0.7775520 0.7889042 3916.512 0.6792399 0.6252453
TX 0.6891429 0.6613613 3342.050 0.5400387 0.4696848
UT 0.7539847 0.6963803 3294.473 0.5703807 0.4290398
VA 0.7521194 0.7338322 3361.210 0.6807386 0.5928361
VT 0.6265539 0.6390542 3714.588 0.4465839 0.3841411
WA 0.5739721 0.5853676 3846.333 0.6370844 0.2521342
WI 0.7706411 0.7294434 3733.691 0.5649803 0.4381035
WV 0.5950661 0.6023062 3303.646 0.6018508 0.3511603
WY 0.7025218 0.6280920 3225.247 0.5342552 0.6524600

Wagner-Peyser

State Employment Rate 2nd Quarter after Exit Employment Rate 4th Quarter after Exit Median Earnings 2nd Quarter after Exit
AK 0.6233091 0.5738131 6703.195
AL 0.6870492 0.6454789 4771.001
AR 0.6951276 0.6821530 5500.067
AZ 0.6130964 0.5688061 5770.490
CA 0.5755927 0.5583262 7583.525
CO 0.6095606 0.5941222 6503.660
CT 0.6072360 0.5887493 6651.074
DC 0.5388145 0.5746261 6686.078
DE 0.6224839 0.6164999 5260.453
FL 0.6287136 0.6053229 5659.150
GA 0.6635193 0.6714540 5611.493
HI 0.4870224 0.5213163 7268.595
IA 0.6318237 0.6736117 6757.601
ID 0.6567834 0.6623638 6206.579
IL 0.6130811 0.6185838 6820.522
IN 0.7872502 0.6987061 7597.555
KS 0.6650821 0.6486789 5781.153
KY 0.6043412 0.5792911 5976.111
LA 0.5332874 0.5268112 4855.397
MA 0.5892770 0.5695919 8057.051
MD 0.6615933 0.6416501 6991.878
ME 0.6261635 0.6146686 5990.206
MI 0.6777842 0.6569479 6916.633
MN 0.6144880 0.6386661 8725.649
MO 0.6478362 0.6501345 5736.045
MS 0.7104870 0.6922309 4385.550
MT 0.7235846 0.6374186 5825.753
NC 0.6781161 0.6708794 5410.355
ND 0.6638404 0.6540913 7140.016
NE 0.7162868 0.6916174 7035.165
NH 0.6351909 0.5784348 8223.827
NJ 0.4786017 0.4738228 5838.370
NM 0.6028099 0.5789728 5470.740
NV 0.6597762 0.6231248 5511.579
NY 0.6087064 0.5820181 7110.259
OH 0.6683462 0.6685371 7379.644
OK 0.5911651 0.5812304 6188.759
OR 0.6436581 0.6315005 6874.122
PA 0.6138479 0.6234369 6471.628
PR 0.3928270 0.3764404 2263.631
RI 0.6072160 0.6437722 7525.168
SC 0.6607746 0.6498059 5246.284
SD 0.6416462 0.6412870 5095.450
TN 0.6598666 0.6707543 5748.262
TX 0.6300789 0.6294347 6298.824
UT 0.6827162 0.6360194 7472.055
VA 0.6886435 0.6792837 6261.153
VT 0.6309326 0.5130067 6799.860
WA 0.6321265 0.6156421 7868.769
WI 0.7125355 0.7059625 6442.446
WV 0.5642697 0.5259337 5108.254
WY 0.6360651 0.5831030 5741.652

All Predictions

Prediction Plots

The plots below show performance of the selected PY 2022-2023 models in predicting actual PY 2020 performance. Note: these predictions are different than the Estimate0 predictions for the PY 2022-2023 models which use PY 2020 participant data but use the most recent economic conditions data instead of data that is aligned to PY 2020.

Adult

Dislocated Worker

Youth

Wagner-Peyser

Full Variable Table

Variable Names
Adult and Dislocated Worker
Youth
Wagner-Peyser
Model Variable Full Variable Name ERQ2 ERQ4 MEQ2 CRED MSG ERQ2 ERQ4 MEQ2 CRED MSG ERQ2 ERQ4 MEQ2
female Female x x x x x x x x x x x x x
age1415 Age 14 to 15 x x x x x
age1617 Age 16 to 17 x x x x x
age1819 Age 18 to 19 x x x x x
age2021 Age 20 to 21 x x x x x
age2544 Age 25 to 44 x x x x x x x x
age4554 Age 45 to 54 x x x x x x x x
age5559 Age 55 to 59 x x x x x x x x
age60 Age 60 or more x x x x x x x x
hispanic Hispanic Ethnicity x x x x x x x x x x x x x
raceasian Race: Asian x x x x x x x x x x x x x
raceblack Race: Black x x x x x x x x x x x x x
racehpi Race: Hawaiian or Pacific Islander x x x x x x x x x x x x x
raceai Race: American Indian x x x x x x x x x x x x x
racemulti Race: Multiple x x x x x x x x x x x x x
hsgrad Highest Grade Completed: High School Equivalency x x x x x x x x x x x x x
collegedropout Highest Grade Completed: Some College x x x x x x x x x x x x x
certotherps Highest Grade Completed: Certificate or Other Post-Secondary Degree x x x x x x x x x x x x x
associate Highest Grade Completed: Associate Degree x x x x x x x x
ba Highest Grade Completed: Bachelor Degree x x x x x x x x
associateorba Highest Grade Completed: Associate or Bachelor Degree x x x x x
gradschool Highest Grade Completed: Graduate Degree x x x x x x x x
empentry Employed at Program Entry x x x x x x x x x x x x x
edstatentry In School at Program Entry x x x x x x x x x x x x x
disabled Individual with a Disability x x x x x x x x x x x x
veteran Veteran x x x x x x x x
englearner Limited English Proficiency x x x x x x x x x x x x
singleparent Single Parent x x x x x x x x
lowinc Low Income x x x x x x x x x x x x
homeless Homeless x x x x x x x x x x x
offender Individual who was Incarcerated x x x x x x x x x x x x
dishomemaker Displaced Homemaker x x x x x x x
yfoster Foster Care Youth x x x x
yparent Youth Parent or Pregnant Youth x x x x x
basiclitdeficient Skills/Literacy Deficient at Program Entry x x x x x
recwages2qprior Received Wages 2 Quarters Prior to Participation x x x x x x x x
wages2qprior Wages 2 Quarters Prior to Participation x x x
longtermunemp Long-Term Unemployed at Program Entry x x x x x x x x x x x x x
uiclaimant UI Claimant x x x x x x x x x x x x
uiexhaustee UI Exhaustee x x x x x x x x x x x
recotherasst Received Other Public Assistance x x x x x x x x x x x x x
recssi SSI or SSDI Recipient x x x x x x x x x x x x x
rectanf TANF Recipient x x x x x x x x x x x x x
ynaa Youth Needing Additional Assistance x x x x x
daysinprog_over1 Percent with More than 1 Day in Program x x x x x x x x x x x
daysenrolled Median Days Enrolled in Education or Training x x
daysenrolled_under30 Percent Enrolled in Education or Training Under 30 Days x x
natresources Natural Resources Employment x x x x x x x x x x x x x
construction Construction Employment x x x x x x x x x x x x x
manufacturing Manufacturing Employment x x x x x x x x x x x x x
information Information Services Employment x x x x x x x x x x x x x
financial Financial Services Employment x x x x x x x x x x x x x
business Professional and Business Services Employment x x x x x x x x x x x x x
edhealthcare Educational or Health Care Employment x x x x x x x x x x x x x
leisure Leisure, Hospitality, or Entertainment Employment x x x x x x x x x x x x x
otheremp Other Services Employment x x x x x x x x x x x x x
publicadmin Public Administration x x x x x x x x x x x x x
ur Unemployment Rate (Not Seasonally Adjusted) x x x x x x x x x x x x x