Individual Income Tax 2 KRS 141.010, enacted October 1, 1942 Flat - PDF document

8/9/2019 1 INDIVIDUAL INCOME TAX THOMAS JONES, PH.D. AUGUST 9, 2019 Office of State Budget Director Individual Income Tax 2 • KRS 141.010, enacted October 1, 1942 • Flat 5% rate on taxable income • IIT is the largest tax receipts account in the General Fund • FY19 receipts were $4,544.7 million • IIT is composed of 4 components: Withholding, Declarations, Net Returns, and Fiduciary • Withholding makes up approximately 94% of total IIT receipts 1

8/9/2019 2018 Tax Law Changes 3 • RS 2018 HB 487 • Changed to flat 5% rate • Update to IRC December 31, 2017 • Pension Exclusion decreased to $31,110 • Elimination of Domestic Production Activities Deduction • Eliminated numerous itemized deductions • Eliminated Personal Credit ($10) for primary, spouse and dependents 2019 Tax Law Changes 4 RS 2019 HB 354 • • Updated to IRC December 31, 2018 • Heavy equipment inventory credit (effects shared across IIT, Corp & LLET) • Restored investment income and wagering losses deductions • Increased the Low-Income Tax Credit • Section 179 Expensing FY20: -$132.0 million • FY21: -$148.4 million • FY22: -$144.3 million • FY23: -$144.3 million • FY24: -$144.3 million • 2

8/9/2019 IIT Actual vs. Official Estimate $millions, NSA 5 ACTUAL EST. $ DIFF % DIFF FY18 4,603.6 4,509.0 -94.6 -2.1 FY19 4,544.7 4,531.2 -13.5 -0.3 Withholding: History $millions, NSA 6 FY WITH % chg FY IIT % chg FY10 3,138.3 -2.1 FY10 3,154.5 -4.9 FY11 3,289.0 4.8 FY11 3,417.8 8.3 FY12 3,464.9 5.3 FY12 3,512.1 2.8 FY13 3,523.6 1.7 FY13 3,723.0 6.0 FY14 3,582.9 1.7 FY14 3,749.3 0.7 FY15 3,793.7 5.9 FY15 4,069.5 8.5 FY16 3,974.7 4.8 FY16 4,282.1 5.2 FY17 4,113.4 3.5 FY17 4,393.9 2.6 FY18 4,248.4 3.3 FY18 4,603.6 4.8 FY19 4,144.7 -2.4 FY19 4,544.7 -1.3 3

8/9/2019 Withholding: Methodology 7 Withholding receipts = f(Kentucky Wages and Salaries) • OLS • Seasonally Adjusted • First-differenced to achieve stationarity • Autocorrelation detected and corrected KY Wages and Salaries: 3 Scenarios $millions, NSA, MAK 8 120,000.0 KYWS_0 KYWS_OPT KYWS_PES 100,000.0 80,000.0 60,000.0 40,000.0 20,000.0 0.0 2001Q1 2001Q3 2002Q1 2002Q3 2003Q1 2003Q3 2004Q1 2004Q3 2005Q1 2005Q3 2006Q1 2006Q3 2007Q1 2007Q3 2008Q1 2008Q3 2009Q1 2009Q3 2010Q1 2010Q3 2011Q1 2011Q3 2012Q1 2012Q3 2013Q1 2013Q3 2014Q1 2014Q3 2015Q1 2015Q3 2016Q1 2016Q3 2017Q1 2017Q3 2018Q1 2018Q3 2019Q1 2019Q3 2020Q1 2020Q3 2021Q1 2021Q3 2022Q1 2022Q3 2023Q1 2023Q3 2024Q1 4

8/9/2019 Withholding: 3 Scenarios $, NSA, GOEA 9 1,400,000,000 with_0 with_pes with_opt 1,200,000,000 1,000,000,000 800,000,000 600,000,000 400,000,000 200,000,000 0 2001Q1 2001Q3 2002Q1 2002Q3 2003Q1 2003Q3 2004Q1 2004Q3 2005Q1 2005Q3 2006Q1 2006Q3 2007Q1 2007Q3 2008Q1 2008Q3 2009Q1 2009Q3 2010Q1 2010Q3 2011Q1 2011Q3 2012Q1 2012Q3 2013Q1 2013Q3 2014Q1 2014Q3 2015Q1 2015Q3 2016Q1 2016Q3 2017Q1 2017Q3 2018Q1 2018Q3 2019Q1 2019Q3 2020Q1 2020Q3 2021Q1 2021Q3 2022Q1 2022Q3 2023Q1 2023Q3 2024Q1 Withholding: Forecast $millions, NSA, GOEA 10 CON % chg OPT % chg PES % chg FY16 3,974.7 4.8 3,974.7 4.8 3,974.7 4.8 FY17 4,113.4 3.5 4,113.4 3.5 4,113.4 3.5 FY18 4,248.4 3.3 4,248.4 3.3 4,248.4 3.3 FY19 4,144.7 -2.4 4,144.7 -2.4 4,144.7 -2.4 FY20 4,286.1 3.4 4,282.3 3.3 4,258.1 2.7 FY21 4,409.9 2.9 4,417.0 3.1 4,311.4 1.3 FY22 4,536.6 2.9 4,557.7 3.2 4,422.8 2.6 FY23 4,659.8 2.7 4,696.1 3.0 4,534.8 2.5 FY24 4,782.1 2.6 4,833.6 2.9 4,667.9 2.9 5

8/9/2019 Declarations: Methodology 11 Declarations receipts = f(Industrial Production Index: all Manufacturing, KY Personal Income, Prime Rate at Commercial Banks, DECLDUMMY) • OLS • Seasonally Adjusted • First differenced to achieve stationarity • No autocorrelation detected Declarations: Forecast $millions, NSA, GOEA 12 DECL % chg FY10 363.4 -19.9 FY11 448.6 23.4 FY12 385.5 -14.1 FY13 469.9 21.9 FY14 439.0 -6.6 FY15 506.0 15.2 FY16 557.8 10.2 FY17 542.1 -2.8 FY18 617.1 13.8 FY19 540.1 -12.5 FY20 555.1 2.8 FY21 583.6 5.1 FY22 607.4 4.1 FY23 626.0 3.1 FY24 645.6 3.1 6

8/9/2019 Net Returns & Fiduciary: Forecast $millions, NSA, GOEA 13 NETR % chg FID % chg FY16 -258.4 7.0 8.0 -29.2 FY17 -263.4 1.9 1.8 -77.9 FY18 -265.6 0.8 3.7 107.3 FY19 -143.1 -46.1 3.0 -19.1 FY20 -179.0 25.1 2.8 -5.6 FY21 -165.9 -7.3 3.1 12.3 FY22 -147.7 -11.0 3.0 -5.5 FY23 -164.2 11.2 3.0 0.0 FY24 -159.3 -3.0 3.0 1.9 IIT Control $millions, NSA, GOEA 14 FY18 % chg FY19 % chg WITH 4,248.4 3.3 4,144.7 -2.4 DECL 617.1 13.8 540.1 -12.5 NETR -265.6 0.8 -143.1 -46.1 FID 3.7 107.3 3.0 -19.1 IIT 4,603.6 4.8 4,544.7 -1.3 FY20 % chg FY21 % chg FY22 % chg FY23 % chg FY24 % chg WITH 4,286.1 3.4 4,409.9 2.9 4,536.6 2.9 4,659.8 2.7 4,782.1 2.6 DECL 555.1 2.8 583.6 5.1 607.4 4.1 626.0 3.1 645.6 3.1 NETR -179.0 25.1 -165.9 -7.3 -147.7 -11.0 -164.2 11.2 -159.3 -3.0 FID 2.8 -5.6 3.1 12.3 3.0 -5.5 3.0 0.0 3.0 1.9 IIT 4,664.9 2.6 4,830.7 3.6 4,999.3 3.5 5,124.6 2.5 5,271.5 2.9 7

8/9/2019 IIT Pessimistic $millions, NSA, GOEA 15 FY18 % chg FY19 % chg WITH 4,248.4 3.3 4,144.7 -2.4 DECL 617.1 13.8 540.1 -12.5 NETR -265.6 0.8 -143.1 -46.1 FID 3.7 107.3 3.0 -19.1 IIT 4,603.6 4.8 4,544.7 -1.3 FY20 % chg FY21 % chg FY22 % chg FY23 % chg FY24 % chg WITH 4,258.1 2.7 4,311.4 1.3 4,422.8 2.6 4,534.8 2.5 4,667.9 2.9 DECL 555.1 2.8 583.6 5.1 607.4 4.1 626.0 3.1 645.6 3.1 NETR -179.0 25.1 -165.9 -7.3 -147.7 -11.0 -164.2 11.2 -159.3 -3.0 FID 2.8 -5.6 3.1 12.3 3.0 -5.5 3.0 0.0 3.0 1.9 IIT 4,636.9 2.0 4,732.2 2.1 4,885.5 3.2 4,999.6 2.3 5,157.3 3.2 IIT Optimistic $millions, NSA, GOEA 16 FY18 % chg FY19 % chg WITH 4,248.4 3.3 4,144.7 -2.4 DECL 617.1 13.8 540.1 -12.5 NETR -265.6 0.8 -143.1 -46.1 FID 3.7 107.3 3.0 -19.1 IIT 4,603.6 4.8 4,544.7 -1.3 FY20 % chg FY21 % chg FY22 % chg FY23 % chg FY24 % chg WITH 4,282.3 3.3 4,417.0 3.1 4,557.7 3.2 4,696.1 3.0 4,833.6 2.9 DECL 555.1 2.8 583.6 5.1 607.4 4.1 626.0 3.1 645.6 3.1 NETR -179.0 25.1 -165.9 -7.3 -147.7 -11.0 -164.2 11.2 -159.3 -3.0 FID 2.8 -5.6 3.1 12.3 3.0 -5.5 3.0 0.0 3.0 1.9 IIT 4,661.1 2.6 4,837.9 3.8 5,020.4 3.8 5,160.9 2.8 5,323.0 3.1 8

8/9/2019 17 SALES TAX GREG HARKENRIDER AUGUST 9, 2019 Office of State Budget Director Historical Sales Tax Growth (FY 2005 through FY 2019) 18 10.0% 9.2% 8.0% 6.0% 6.0% 6.0% 6.0% 5.4% 4.4% 3.7% 3.6% 4.0% 3.5% 2.5% 2.1% 2.0% 0.7% 0.0% -0.7% -1.0% -2.0% -2.2% -4.0% 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 9

8/9/2019 Sales Tax with New Tax Law (Quarterly Data, Millions $) 19 SALES_SA 2018q2 1,100 1,000 900 800 700 08 09 10 11 12 13 14 15 16 17 18 19 20 Reminder of Tax Reform 20  Base Broadening  Selected Services  Installation and Repair  Online Retailers and Marketplace Providers  Estimated Fiscal Impacts of Tax Reform  FY19 – $208.2 million  FY20 – $264.3 million  FY21 – $274.6 million  FY22 – $275.8 million  FY23 – $276.3 million  FY24 – $276.8 million 10

8/9/2019 Modeling Strategy 21  Dependent Variable with non-static policy regimes  Too early for dummy variables  Too early for switching models  Approach Taken:  Create policy-neutral sales tax variable for projections  Withhold the four quarters of “corrupt” data  Forecast with in-sample estimation (Stop estimation at CY 2018q2)  Project the policy-neutral series (2018q3 through 2024q2)  Add back the policy impacts (FY19 through FY24) Justification for Modeling Approach 22  FY18 sales tax estimate was accurate  Estimated FY18 -- $3,611.9  Actual FY18 -- $3,605.7  Model is working in a policy-neutral world  FY19 tax fiscal impacts close to estimated  Estimated FY19 fiscal impact -- $208.2 million  Engineered fiscal impact --$214.9 million (5.3% of the 9.2%)  Engineered fiscal impact  Actual FY2019 collections, minus  Blended control in-sample forecast for FY19 11

8/9/2019 Sales Tax with New Tax Law (Quarterly Data, in Millions $) 23 1,080 1,040 1,000 960 920 880 840 800 2015 2016 2017 2018 2019 2020 2021 2022 SALES_SA SALES_ARIMA SALES_STR_Logs SALES_STRDIFF SALES_STRDIFF2 SALES_SA VAR Types of Modeling for the Sales Tax 24  Quarterly sales tax data have several correctible sources of variation  Seasonality – Census X12  Trend – Differencing  Types of final models (2001q1 – 2018q2)  VAR with Cointegration (Sales and Wages & Salaries)  ARIMA (Control Only)  Structural models  Logs  Differences  Sales_sa = f(cdfhe,kyws,c) 24 12