UI Benefit Financing Seminar Division of Fiscal and Actuarial - PowerPoint PPT Presentation

UI Benefit Financing Seminar Division of Fiscal and Actuarial Services U.S. DOL/ETA/OUI October 23-26, 2018

1. Understanding The Elements That Comprise the Payment of Benefits in Your State. 2. Calculating a Forecast for Total State Benefits. 2

Total Benefits Paid STA TATE LA TE LAW W VA VARI RIABLES LES Co Coverag age / / El Eligibility / / Ben enef efit L Lev evels / / Wag age B Bas ase / e / Tax Tax Rates Rat es / / Tr Trigger ers ECO ECONOMIC SCE CENARIO VA VARI RIABLES Tota otal L l Labo bor Forc orce / / Tota otal U l Unemployment / / Avera rage Earnings / Ear / Interest Rat Rate 3

Answering Three Questions: How Many People are Receiving Benefits? 1. For How Long Do They Receive Benefits? 2. How Much Do They Receive? 3. 4

Unemployment Flow - First Quarter CY2018 (000) MARCH JANUARY FEBRUARY Total Labor Force 161,494 160,037 161,548 Total Unemployed 7,189 7,091 6,671 808 492 UI First Payments 392 Unemployment Insurance Recipients 1,958 (29%) 2,270 (32%) 2,313 (32%) 200 164 161 UI Exhaustees Not in Labor Force 95,549 95,439 96,743 5

1 2 a) How many Insured Unemployment Rate First Claimants? Payments (Avg. # of Claimants / Week) a) How Long Do Total Weeks Average They Claim? Compensated Duration a) How Much Do Average Weekly Benefit Average Weekly They Receive? Benefit Total Benefits 6

Unemployment Trust Fund Modelling Total Benefits Paid Weeks Compensated Avg. Weekly Benefit First Pays Average Duration Weeks Claimed or Coverage / Eligibility / Total Labor Force / Total Unemployment / Benefit Levels / Wage Base / Avg. Wages / Interest Rate Tax Rates/ Triggers Economic Scenario State Law Variables

Unemployment Trust Fund Modelling Weeks Claimed * Time Period (Weeks) Insured Unemployment IUR Covered Employment TUR Coverage / Eligibility / Total Labor Force / Total Unemployment / Benefit Levels / Wage Base / Avg. Wages / Interest Rate Tax Rates/ Triggers Economic Scenario State Law Variables

1) 1) IUR IUR = f( f(TUR UR, o other va variables) IU = IUR IU IUR x x Cove vered Empl ploy oyment 2) 2) IU IU = f( f(TU, U, o other va variables) 3) ) IU/TU TU = = f(TU TUR, R, o other er var ariables) IU = IU = IU/ IU/TU x x TU

 Reg Regres ession M Model eling ◦ Meth thod f for de or dete termin ining th the re rela lati tionships amon mong tw two or more o or more vari riables ◦ Meth thod f for f or fore orecasting f future re v valu lues of of on one var ariab able ( (dep epen endent), g given en the v e val alues o of t the e other v varia iable bles ( (indepe pende dent) t) ◦ *Assume mes h histor toric ical r l relation ionship ips c contin tinue i in the futur ure

Time Series UI Data: IUR & TUR

TUR - IUR Scatter Plot 5.00 Y -- Dependent Variable -- IUR 4.00 3.00 2.00 1.00 0.00 0.00 2.00 4.00 6.00 8.00 10.00 X -- Explanatory Variable -- TUR

TUR - IUR Scatter Plot w/ Trend 5.00 Y -- Dependent Variable -- IUR 4.00 y = a + b * x 3.00 2.00 1.00 0.00 0.00 2.00 4.00 6.00 8.00 10.00 X -- Explanatory Variable -- TUR

Residuals 7.00 Y -- Dependent Variable -- IUR 6.00 5.00 4.00 3.00 2.00 1.00 0.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00 X -- Explanatory Variable -- TUR

TUR - IUR Scatter Plot w/ Trend 4.50 Y -- Dependent Variable -- IUR 4.00 3.50 3.00 2.50 2.00 1.50 1.00 0.50 0.00 5.00 6.00 7.00 8.00 9.00 10.00 X -- Explanatory Variable -- TUR

 y y = a a + b*x ◦ IUR R = = I Interce cept + + Co Coeffici icient(b) * * TU TUR  y = a + b1*x1 *x1 + b2*x2 *x2 + … + bN bN*xN xN

Identify Potentia ial l Ex Expla lanatory ry Va Varia riable les 1. 1. Co Colle llect ct Data (BLS LS, LM LMS, UI Pro rogra gram) 2. 2. Plot and and Revi view Dat Data a and and Relat ationships 3. 3. Choos oose a tim ime perio riod 4. 4. Choose se specifi fication(s) n(s) 5. 5. Ad Add/Drop v var ariables in S Stepwise Ap Approach • Va Valid lidate 6. 6. Te Test fore reca casts 7. 7. Final nal mo mode del 8. 8. Devel elop assum sumptions/ ns/sc scena enario 9. 9. 10. Forecast st 10.

 Bas ased on knowl owledge of UI pro rogra ram ◦ State p e pro rogra ram id idio iosyncrasies ◦ State/Nationa nal E Econo nomy & Rece cessions ns ◦ Seasonalit lity ◦ Structura ral E l Economic ic or Progra rammatic ic S Shif ifts  Avail ilab abili lity o of Data f a for R Regres ressi sion a n and nd Forec ecas astin ing ta & ◦ Hi Histor torical Period od Data ◦ Fo Forecast P Period Dat d Data  Must st h have o or produce projections/ s/assu assumptions s of e each vari riable u used in in re regre ression e equation in in o ord rder to f fore recast.

 TU TUR R – Inclu ludi ding l g lags/l /leads ds  La Lagged IUR IUR  Exhaustion tions  Extende ded d UI B Benefit A it Availa labili bility ty  State l law/admin dminis istra tration tion v varia iable bles  Demogra ograph phic ics / / changin ging i g industri tries  Long g term u m unempl mploy oyed  Manufactu turin ring E g Employ loyment  Union oniz ization tion  Job L Losers rs ( (alternativ tive t to u unemploy mployed) d)

Year.Qtr TUR IUR TU IU … D1 D2 D3 Rececession 1998.3 3.28 1.97 57,666 31,112 … 0 0 0 0 1998.4 2.64 1.73 46,007 27,420 … 0 1 0 0 1999.1 3.38 2.55 58,598 40,715 … 1 0 1 0 1999.2 2.98 1.94 52,254 31,050 … 0 0 0 0 1999.3 2.68 1.96 47,438 31,553 … 0 0 0 0 1999.4 2.47 1.63 43,435 26,337 … 0 1 0 0 2000.1 3.13 2.31 55,011 37,580 … 1 0 1 1 2000.2 2.40 1.60 42,403 26,147 … 0 0 0 1 2000.3 2.27 1.70 40,250 27,840 … 0 0 0 1 2000.4 1.78 1.58 31,188 25,908 … 0 1 0 1

IUR Not Seasonally Adjusted 6.00 5.00 4.00 3.00 2.00 1.00 0.00

IUR Not Seasonally Adjusted TUR Seasonally Adjusted 12.00 10.00 8.00 6.00 4.00 2.00 0.00

IUR Not Seasonally Adjusted TUR Seasonally Adjusted 6.00 12.00 5.00 10.00 4.00 8.00 3.00 6.00 2.00 4.00 1.00 2.00 0.00 0.00

 At At l lea east st 6 to 8 8 yea ears  10 10 to 12 12 years rs is is ge genera rall lly adequate but use dis iscre cretio ion to: ◦ Include A AT T LEA EAST o T one r e reces ecession ( (Co Consider magnitu itude de and c change ges i in U UI r relati tion onships ips)  Look for s stat ate law c aw chang hanges  Lo Look for r other r stru ruct ctura ral l ch changes

 Add/ d/Dr Drop o p one variabl ble a at a time ◦ Stepwi pwise appr approach  “Stepwis ise M Model Tracker.xls xlsx” x”  Chec eck: k: ◦ Coefficie icient nts ◦ Adjust sted R R-Square uare ◦ Residuals uals  Inc nclude vari ariables o of int interest / / hig high im importance

 Significance of Individual Variables: ◦ t Statistic = Coefficient / Standard Error ◦ t Statistic > 2 or P-value of Coefficient < 0.05 ◦ Look for correct sign (+/-) & magnitude of coefficient  Adjusted R Square: ◦ Reflects proportion of variation in dependent variable (TUR) explained by regression line. ◦ Useful to compare performance across multiple regressions ◦ Larger Adj. R Square = “better” fit