Forecasting Tax Revenues in Latvia: Analysis and Models Velga - - PowerPoint PPT Presentation

Forecasting Tax Revenues in Latvia: Analysis and Models

Velga Ozolina, Astra Auzina-Emsina, Remigijs Pocs Riga Technical University, Latvia

Data Analysis

• CSB data
• Ministry of Finance data
• State Revenue Service (SRS)

data

• Eurostat data

Tax Burden in Latvia in 1995-2012, % of GDP

5 10 15 20 25 30 35 %

Data Source: CSB database

Tax Burden in the EU Countries in 2012, % of GDP

10 20 30 40 50 60 Denmark Belgium France Austria Sweden Italy Finland EU-28 Germany Luxembourg Netherlands Hungary Slovenia United … Greece Croatia Cyprus Czech Republic Portugal Malta Spain Estonia Poland Ireland Romania Slovakia Latvia Bulgaria Lithuania %

Data Source: Eurostat database

Tax Revenues in Latvia (ESA95 methodology), m EUR

5000 10000 15000 20000 25000 500 1000 1500 2000 2500 m EUR m EUR Value Added Tax Customs Duties Excise Taxes Personal Income Tax Corporate Income Tax Social Contributions Other Taxes Nominal GDP (right axes)

Data Source: CSB database

Social Contributions in Latvia, m EUR

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 500 1000 1500 2000 2500 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 m EUR ESA95 methodology National methodology Ratio (right axes)

Data Source: CSB database, Ministry of Finance data

Analysis of Legal Aspects

The main laws in the group of direct taxes are:

• On State Social Insurance,
• On Personal Income Tax,
• On Corporate (Enterprise)

Income Tax,

• Micro-enterprise Tax Law.

Analysis of Legal Aspects

• Employed persons by professional status, thsd

Overall statistics (CSB) Taxpayers (SRS)

200 400 600 800 1000 10 20 30 40 50 60 70 80 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 thsd thsd Employers (owners) Self-employed Family workers Self-employed in the second job Employees (workers), right axes 10 20 30 40 50 60 70 80 90 100 100 200 300 400 500 600 700 800 900 1000 I III I III I III I III I III I III I III I III I III I III I III 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 thsd thsd Employed Self-employed, right axes

Data Source: CSB database, State Revenue Service data

Analysis of Legal Aspects

The main laws in the group of indirect taxes are:

• Value Added Tax Law (before

2013 law On Value Added Tax),

• On Excise Duty.

Seasonality Analysis

• Revenues of Direct Taxes, m EUR
• 50

50 100 150 200 250 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 m EUR Social Contributions Personal Income Tax Enterprise Income Tax

Data Source: Ministry of Finance data

Quarterly Seasonal Indexes for Social Contributions

0.85 0.9 0.95 1 1.05 1.1 1.15 1.2 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Q1 Q2 Q3 Q4

Data Source: Ministry of Finance data

Quarterly Seasonal Indexes for Personal Income Tax

0.8 0.85 0.9 0.95 1 1.05 1.1 1.15 1.2 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Q1 Q2 Q3 Q4

Quarterly Seasonal Indexes for Corporate Income Tax

0.3 0.5 0.7 0.9 1.1 1.3 1.5 1.7 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Q1 Q2 Q3 Q4

Seasonality Analysis

• Revenues of Indirect Taxes, m EUR

20 40 60 80 100 120 140 160 180 200 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 m EUR Value Added Tax Excise Duty

Data Source: Ministry of Finance data

Quarterly Seasonal Indexes for Value Added Tax

0.75 0.8 0.85 0.9 0.95 1 1.05 1.1 1.15 1.2 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Q1 Q2 Q3 Q4

Data Source: Ministry of Finance data

Quarterly Seasonal Indexes for Excise Duty

0.6 0.8 1 1.2 1.4 1.6 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Q1 Q2 Q3 Q4

Data Source: Ministry of Finance data

Productivity and Economic Activity Analysis

• Labor productivity and real GDP growth rate

(2004-2007) (2008-2010) (2011-2012)

• .

Methodology

• Modelling Approaches
• Models and Equations

– Monthly – Quarterly – Annual

Monthly Data

• Seasonality Indexes
• Corporate Income Tax

Revenues

Corporate Income Tax Revenues

• CIT revenues = coefmonthly * CIT revenueslag *(1 +

+ PCIinfl/100)/12 + coefmay *PROFlag/100 where CIT revenues – corporate income tax revenues, CIT revenueslag – annual corporate income tax revenues with 17-month lag, coefmonthly – corporate income tax advance payments coefficient, PCIinfl – annual growth rate of private consumption price index in the previous year, coefmay – corporate income tax revenues coefficient applied only in May, PROFlag – annual profit in the previous year.

• 0.5

0.5 1 1.5 2 2.5

• 1000
• 500

500 1000 1500 2000 2500 3000 3500

• 1

1 2 3 4 5 m EUR % Corporate income tax revenues coefficient, % Annual profit in the previous year, m EUR

Corporate Income Tax Revenues

SEE = 0.32 RSQ = 0.7593 RHO = 0.45 Obser = 196 from 1997.009 SEE+1 = 0.28 RBSQ = 0.7530 DW = 1.11 DoFree = 190 to 2013.012 MAPE = 9.70 Variable name Reg-Coef Mexval Elas NorRes Mean Beta 0 LTAX_UIN - - - - - - - - - - - - - - - - - 2.89 - - - 1 intercept 5.45808 5.9 1.89 4.15 1.00 2 @log(PCI[12]) -2.37844 18.5 -3.97 2.90 4.82 -0.904 3 @log(IM[12]) 0.48841 2.1 1.03 1.47 6.09 0.467 4 @log(IM[6]) 0.80975 7.9 1.72 1.26 6.15 0.761 5 D_5*@log(IM[6]) 0.09099 11.2 0.02 1.04 0.51 0.241 6 @log(W_NOM[8]/PCI[8]) 0.86536 1.8 0.31 1.00 1.05 0.375

Quarterly Data

• Identities
• Econometric Equations

Identities

tax_rev = taxr_coef*taxr*tax_base, where tax_rev – tax revenues, taxr_coef – tax rate coefficient, taxr – tax rate, tax_base – tax base.

0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 0.1 0.2 0.3 0.4 0.5 0.6 I III I III I III I III I III I III I III I III I III I III I III I III I III I III I III I III I III I III I III I III 19951996199719981999200020012002200320042005200620072008200920102011201220132014 Tax rate coefficient of the value added tax Tax rate coefficient of excise duty, right axes

0.7 0.75 0.8 0.85 0.9 0.95 I III I III I III I III I III I III I III I III I III I III I III I III I III 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Tax rate coefficient of social contributions Tax rate coefficient of personal income tax

Corporate Income Tax

CIT revenues = coefq * CIT revenueslag *(1 + PCIinfl/100)/12 + + coefII *PROFlag/100, where CIT revenues – corporate income tax revenues, CIT revenueslag – annual corporate income tax revenues with 2- year lag (quarter 1), with 1- year lag (quarters 3 and 4) or weighted average of the 1-year and 2-year lag (quarter 2), coefq – corporate income tax advance payments coefficient, PCIinfl – annual growth rate of private consumption price index in the previous year,

• coefII – corporate income tax revenues coefficient applied only in

the quarter 2,

• PROFlag – annual profit in the previous year.

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 I III I III I III I III I III I III I III I III I III I III I III I III I III I III I III I III I III 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Advance payments coefficient Tax revenues coefficient, right axes

Social Contributions Revenues

SEE = 12.80 RSQ = 0.9911 RHO = 0.43 Obser = 48 from 2002.100 SEE+1 = 11.82 RBSQ = 0.9907 DW = 1.14 DoFree = 45 to 2013.400 MAPE = 2.39 Variable name Reg-Coef Mexval Elas NorRes Mean Beta 0 TAX_SOC - - - - - - - - - - - - - - - - - 394.63 - - - 1 intercept -15.67795 5.9 -0.04 112.06 1.00 2 TAXR_SOC*((EMPL*W_NOM*3)/100000) 0.82441 929.9 1.03 1.22 492.62 1.007 3 D_EU 13.39648 10.3 0.01 1.00 0.31 0.046

Personal Income Tax Revenues

SEE = 10.34 RSQ = 0.9841 RHO = 0.14 Obser = 48 from 2002.100 SEE+1 = 10.34 RBSQ = 0.9834 DW = 1.72 DoFree = 45 to 2013.400 MAPE = 3.24 Variable name Reg-Coef Mexval Elas NorRes Mean Beta 0 TAX_INC_PERS - - - - - - - - - - - - - - - - - 246.31 - - - 1 intercept -15.42434 9.4 -0.06 62.85 1.00 2 TAXR_IIN*((EMPL*(W_NOM-TAX_NMIN))/1000-TAX_SOC*TAX_SOC_E) 3.39666 687.7 1.08 2.44 78.20 1.022 3 D_10 -46.54480 56.2 -0.02 1.00 0.08 -0.157

Corporate Income Tax Revenues

SEE = 0.26 RSQ = 0.8398 RHO = 0.21 Obser = 72 from 1996.100 SEE+1 = 0.25 RBSQ = 0.8303 DW = 1.58 DoFree = 67 to 2013.400 MAPE = 5.23 Variable name Reg-Coef Mexval Elas NorRes Mean Beta 0 LTAX_INC_CORP - - - - - - - - - - - - - - - - - 3.92 - - - 1 intercept -8.75285 40.2 -2.23 6.24 1.00 2 @log(PI_CONS_PR[4]) -2.03484 27.4 0.19 3.47 -0.36 -0.916 3 @log(IM_CP[4]) 0.77342 6.9 1.43 1.45 7.26 0.817 4 @log(IM_CP[2]) 0.85733 10.0 1.60 1.13 7.32 0.876 5 D_2*@log(INV_CP[1]) 0.03463 6.4 0.01 1.00 1.58 0.150

Value Added Tax Revenues

SEE = 0.10 RSQ = 0.9723 RHO = 0.13 Obser = 76 from 1995.100 SEE+1 = 0.09 RBSQ = 0.9712 DW = 1.74 DoFree = 72 to 2013.400 MAPE = 1.37 Variable name Reg-Coef Mexval Elas NorRes Mean Beta 0 LTAX_VAT - - - - - - - - - - - - - - - - - 5.27 - - - 1 intercept -2.56866 55.4 -0.49 36.14 1.00 2 @log(GDP_CP) 1.03000 201.5 1.54 1.28 7.89 1.094 3 @log(TIME) -0.09040 6.8 -0.06 1.13 3.37 -0.143 4 D_EU 0.09103 6.5 0.00 1.00 0.20 0.063

Excise Duty Revenues

SEE = 0.08 RSQ = 0.9767 RHO = 0.06 Obser = 60 from 1999.100 SEE+1 = 0.08 RBSQ = 0.9759 DW = 1.87 DoFree = 57 to 2013.400 MAPE = 1.25 Variable name Reg-Coef Mexval Elas NorRes Mean Beta 0 LTAX_EXC - - - - - - - - - - - - - - - - - 4.70 - - - 1 intercept -3.29918 170.7 -0.70 42.90 1.00 2 @log(GDP_CP) 0.98389 514.3 1.70 1.39 8.11 0.958 3 D_0910 0.14311 17.7 0.00 1.00 0.13 0.098

Annual Data

• Calculations in ESA95
• Identities
• Transformation Coefficients to

forecast national data

Dynamics of Estimated II Pillar Rates in 2003-2013

• 0.05

0.05 0.1 0.15 0.2 0.25 0.3 20032004200520062007200820092010201120122013 Estimated II pillar rate (based

• n assets)

Estimated II pillar rate (based

• n differences in national and

ESA95 methodologies) Estimated II pillar rate (combined) Max II pillar rate

Tax Rate Coefficients

• 0.5

0.0 0.5 1.0 1.5 2.0 2.5 Tax rate coefficient of personal income tax Tax rate coefficient of corporate income tax Tax rate coefficient of social contributions 0.00 0.02 0.04 0.06 0.08 0.10 0.0 0.1 0.2 0.3 0.4 0.5 0.6 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Tax rate coefficient of the value added tax Tax rate coefficient of excise duty, right axes

Forecasts

• Evaluation of precision
• Numbers

Corporate Income Tax (Monthly Data)

2014.001 – 2014.007

• MAPE = 14.3%
• Modified dummy MAPE = 3.6%

MAPE Values, % (Quarterly Data)

Tax Type 2014 I 2014 I and II Social Contributions 3.7 2.2 Personal Income Tax 6.8

• Corporate Income Tax

0.4 2.5 Value Added Tax 15.4 16.5 Excise Duty 0.7 2.0

Comparison of Forecasts

Tax Type Identity-based approach Econometric equations Monthly Quarterly Annual Monthly Quarterly Social Contributions

• 2271.3

2139.6

• 2237.2

Personal Income Tax

• 1379.3

1415.1

• 1385.0

Corporate Income Tax 342.3 359.2 350.0 359.5 354.6 Value Added Tax

• 1885.1

1738.7

• 1638.5

Excise Duty

• 757.1

731.2

• 777.9

Conclusions

• Using annual data, identity-based approach

should be prefered, however quarterly and monthly data can give similar results and thus the choice is in hands of the model-user

• Identity-based approach allows for a greater

flexibility in scenario-building process. Econometric approach involves less assumptions and thus may seem to be more

• bjective
• Forecasts depend very much on the values of

exogenous indicators, therefore modelling approaches should be tested regulary to find the most reliable ones

LAIMA

• Latvian Interindustry Model

(Aggregated/Annual)

• Goddess of destiny

Thank You for Attention

Questions?