January, 2014 Results are preliminary. Comments welcome. Milligan - - PowerPoint PPT Presentation

Milligan and Smart: High Income Taxation 1

Estimating high-income tax elasticities using sub-national variation in tax rates

Kevin Milligan Vancouver School of Economics University of British Columbia Michael Smart Department of Economics University of Toronto January, 2014

Results are preliminary. Comments welcome.

Milligan and Smart: High Income Taxation 2

How do individuals respond to higher income tax rates?

Real responses Labour supply consumption/savings. Evidence variable, but effects in many cases limited. Shifting responses. Timing Accounting / form / arbitrage  Some effects very large.

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Who Responds? \

Real:  Anyone can adjust labour supply or consumption / savings.  Do hyper-competitive top lawyers and hedge fund workers really cut hours worked in response to taxes? Shifting / avoidance  Shifting responses assisted by expensive advice, costly financial structures.

 Advantage goes to higher incomes:

Do we expect those with access to the very best tax advice to fail to heed it?

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Veall (2012) CEA Presidential Address

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Any role for taxes in the ‘surge’?

In United States:  Time series lines up fairly well with Reagan tax cuts in early 80s and especially TRA 1986.  Piketty and Saez are skeptical though…. In Canada:  Veall: taxes don’t seem to matter a lot in explaining the trends

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Questions posed in this paper

Q: What impact have taxes had on high income trends? Q: How much success would taxes have in pushing against high income trends?  

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Preview of findings

 We find substantial elasticities, concentrated at higher incomes

• Elasticities for P99.9 over 1.0; 4 times that of P99-P99.9.

 Veall is right—taxes don’t explain the ‘surge very well.

• But, the tax cuts of the 2000s can account for about one third of top

income share growth in that decade.  Some evidence that Alberta might be different; capital and earned income affected. 

• Starts to uncover some hints about mechanisms.

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Outline of talk:

• 1. Brief review of other evidence
• 2. Quick tax backgrounder
• 3. Empirical strategy / Identification
• 4. Data
• 5. Main estimation results
• 6. Policy implications
• 7. Extensions: Alberta and composition of income.

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Other evidence:

Survey by Saez Slemrod and Giertz 2012: While there are no truly convincing estimates of the long-run elasticity, the best available estimates range from 0.12 to 0.40. Proceeding mechanically, at the approximate midpoint of this rate—an ETI of 0.25 Canadian evidence:  Sillamaa and Veall (2001) 1.67 at the top  Gagne, Nadeau, and Vaillancourt (2000)

• ver 3.0!

 Saez and Veall (2005) Big / small  Department of Finance (2010) around 0.6

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Canadian Personal Income Taxes

Two important features:

• 1. Tax base is co-occupied by federal government and provinces.

 In 2009, 39.6% of the $189B in income tax went to provinces.

• 2. Tax Collection Agreements impose common tax base on provinces.

 Quebec not ‘agreeing province’; differences are fairly small.

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Tax brackets in BC 2010:

Combined federal / provincial brackets and rates Taxable Income BC Tax Federal Tax Combined Tax Up to $35,859 5.06% 15% 20.06% …up to $40,970 7.7% 15% 22.7% …up to $71,719 7.7% 22% 29.7% …up to $81,941 10.5% 22% 32.5% …up to $82,342 10.5% 26% 36.5% …up to $99,987 12.29% 26% 38.29% …up to $127,021 14.7% 26% 40.7% …over $127,021 14.7% 29% 43.7% Canada features almost no progressivity for incomes in the ‘top 1%’ over our time period.  Relieves us a bit from worrying about how to measure tax rate for those in top 1%

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Figure 1: Top Tax Rates for 2013 by Province

Source: Canadian Tax and Credit Simulator

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Figure 2: time path of top rate

Source: Canadian Tax and Credit Simulator

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Empirical Strategy

Interested in the following economic relationship: ( ) With some manipulation, this can be written in the form of a group share of total income , in log form to directly estimate elasticity e:

( )

Why use shares? Saez (2004):  Against: not the same people in the share each year  For: estimating shape of income distribution for tax policy analysis, is ok.  Against: time series identification is very tricky.

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Saez, Slemrod, and Giertz 2012 JEL:

researchers should be seeking better sources of identification; for example, parallel income tax systems that differentially affect taxpayers over a long period of time.

Milligan and Smart (2014): OK! We can compare across provinces:

( )

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What do we get out of introducing multiple jurisdictions?

• 1. Same tax base

The elasticity e is a function of a given tax system. Comparing across tax systems and you’re not measuring the same thing.

• 2. Control for common time effects in a flexible way.

We can have our time effects and even try provincial polynomial trends.

…but we’re not the first on this:

 Atkinson and Leigh (2010) compare across 5 anglo-sphere countries  Finance (2010) do what we do with Canada.

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Two more empirical manipulations

• 1. We use the log share ratio as extra control

Using income shares

controls for trends in complement group to g:

( (

)

⁄ ) ( )

• 2. We instrument for using a simulated tax rate based on an income level common

across all provinces and years This is standard in the literature, but almost irrelevant here since top tax bracket rarely above P99 cutoff.

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Data

Main source of data is the CANSIM high income database (204-0002)  Ultimate source is Longitudinal Administrative Database (taxfiler data)  20 percent sample of Canadian taxfilers between 1982 and 2011.  We use ‘total income excluding capital gains’ definition Taxes are calculated using Canadian Tax and Credit Simulator (CTaCS)  Available 1962 to 2012  Don’t have to worry about refundable tax rates or many other issues for these high-income people.  Simulated all provinces/years using common 2000 income distribution Also use 1991-2010 ‘Income Statistics’ put out by CRA Taxes are calculated using Canadian Tax and Credit Simulator (CTaCS)  Reports total amount and total people for series of nominal categories.

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Table 1: Top Income Thresholds

1988 2011 P95 P99 P99.9 P95 P99 P99.9 Canada 88,520 154,133 444,893 105,229 203,656 677,430 NL 71,667 108,810 98,039 168,094 PE 70,031 114,046 81,715 139,722 NS 78,212 129,099 333,792 90,266 154,588 399,831 NB 74,121 116,173 86,282 147,010 363,395 QC 81,648 133,190 316,612 91,140 169,649 505,254 ON 95,720 179,495 579,882 108,047 215,316 778,189 MB 78,376 125,336 310,394 91,917 161,098 469,595 SK 79,357 125,663 288,796 107,269 180,240 507,878 AB 92,284 160,515 416,422 138,945 281,096 1,044,419 BC 89,502 157,897 457,001 102,411 190,151 650,418 Source: CANSIM 204-0002

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Figure 3: Top Income Shares 1988-2011

Source: CANSIM 204-0002

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Figure 4: Top Income Share Index 1988-2011

Source: CANSIM 204-0002

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Results overview

 Results focused on the log-income share equation; main parameter of interest is the elasticity of reported income.  Start with a basic ‘time series’ specification like the literature to show value

• f having provinces there.

 Try out:

• Different fractiles
• Different time periods
• Different income definitions

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Figure 5: Basic Scatter Plot

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Table 2: Comparing Time Series to Prov-Year

(1) (2) (3) (4) (5) National Time Series Province-Year Data No Add US Province Add US Add time Controls top 1% controls top 1% controls Observations 24 24 240 240 240 R-squared 0.473 0.849 0.71 0.875 0.941 Log Actual MTR 2.032*** 0.485 1.664*** 0.505* 1.082* [0.319] [0.322] [0.361] [0.241] [0.496] Log US Top One Percent Share

• 5.690***
• 5.130***

[0.809] [0.492]

Source: CANSIM Series 204-0002 and CTaCS

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Table 3: Different specifications

(1) (2) (3) (4) (5) (6) Basic IV Add income Unweighted Log share Linear Quadratic Dependent Provincial Provincial Variable Trends Trends Observations 240 240 240 240 240 240 R-squared 0.941 0.97 0.949 0.97 0.975 0.988 Log Actual MTR 1.068** 0.689*** 0.723** 0.640*** 0.794** 0.510* [0.441] [0.238] [0.293] [0.210] [0.323] [0.264] Log Total 0.729*** 0.805*** 0.631*** 0.903*** 1.523*** Income [0.0767] [0.0341] [0.0690] [0.179] [0.209] Source: CANSIM Series 204-0002 and CTaCS

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Table 4: Different Time Periods

Base 1988- 1999 2001- 2011 1995- 2005 Observations 240 120 110 110 R-squared 0.97 0.961 0.972 0.978 Log Actual MTR 0.689*** 0.948** 1.026 0.458** [0.238] [0.390] [0.829] [0.179] Log Total 0.729*** 1.257*** 0.481*** 0.926*** Income [0.0767] [0.312] [0.118] [0.242]

Source: CANSIM Series 204-0002 and CTaCS

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Table 5: Different Income Definitions

(1) (2) (3) (4) Total Income Market Income No Capital With Capital No Capital With Capital Gains Gains Gains Gains Observations 240 240 240 240 R-squared 0.97 0.96 0.964 0.953 Log Actual MTR 0.689*** 0.817** 0.723*** 0.791** [0.238] [0.364] [0.243] [0.335] Log Total 0.729*** 0.766*** 0.605*** 0.643*** Income [0.0767] [0.0990] [0.0527] [0.0735] Source: CANSIM Series 204-0002 and CTaCS

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Table 6: Different High Income Fractiles

(1) (2) (3) (4) P90 P95 P99 P99.9 Observations 240 240 240 190 R-squared 0.962 0.969 0.97 0.952 Log Actual MTR 0.0246 0.221 0.689*** 1.451*** [0.219] [0.218] [0.238] [0.541] Log Total 0.424*** 0.511*** 0.729*** 0.893*** Income [0.0533] [0.0636] [0.0767] [0.162]

Source: CANSIM Series 204-0002 and CTaCS

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Table 7: Interior High Income Fractiles

(1) (2) (3) (4) P90-P95 P95-P99 P99-P99.9 P99.9+ Observations 240 240 240 190 R-squared 0.738 0.926 0.973 0.952 Log Actual MTR

• 0.211**
• 0.107

0.313** 1.413*** [0.0874] [0.108] [0.123] [0.523] Log Total

• 0.0369

0.108*** 0.452*** 0.844*** Income [0.0306] [0.0282] [0.0313] [0.156]

Source: CANSIM Series 204-0002 and CTaCS

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Making sense of the magnitudes What does an elasticity of 0.7 for P99 imply? What about 1.45 for P99.9? Use ‘revenue maximizing tax rate’ as a benchmark. (Laffer Curve peak) Saez Slemrod and Giertz provide the derivation of the formula: where e is the elasticity we’ve been estimating a is a parameter of the income distribution.

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How much scope to raise top tax rates?

Taking 1 for Canada and our estimates…. For P99, e of 0.689 gives For P99.9, e of 1.451 gives 27.5% For P99-P99.9, e of 0.313 gives

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Another exercise to gauge the magnitude

What would the time path for P99 have looked like if taxes had stayed at their 1988 level all the way to 2010? This helps to address the question about the importance of taxes in understanding what is going on with income concentration. Also, try holding taxes at 1999 levels for the 2000s.

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Figure 6: Predicted time-path for P99 with 1988 taxes

Source: CANSIM 204-0002 and CTaCS

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Figure 7: Predicted time-path for P99 with 1999 taxes

Source: CANSIM 204-0002 and CTaCS

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Extension to Alberta

Alberta has the lowest provincial tax rate for high earners: 10%. Residents of other provinces could potentially save a lot by shifting money to Alberta. This is an extra source of leakage for other provinces that doesn’t exist in Alberta. Does this interprovincial shifting potentially explain why we are getting such a high elasticity estimate?

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Table 8: Differential impact in Alberta

(1) (2) (3) (4) (5)

Base AB AB West East interaction interaction P99 P99 P99.9 P99 P99 Observations 240 240 190 120 120 R-squared 0.968 0.969 0.954 0.964 0.954 Log Actual MTR 0.615* 0.619** 1.492* 0.893* 0.0174 [0.275] [0.270] [0.637] [0.340] [0.139] AB 0.849*** 0.802*** [0.0579] [0.140] AB*log MTR

• 0.504
• 1.406*

[0.453] [0.655] Log Total 0.730*** 0.863*** 1.264*** 0.661*** 1.396** Income [0.0872] [0.177] [0.284] [0.0964] [0.499]

Source: CANSIM Series 204-0002 and CTaCS

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Composition: how do different types of income react?

Beyond looking at Total Income, it is instructive to consider how other income measures react to taxation. Here’s what we do:

 Use the CRA Income Statistics universe data  Impute to get P99, P99.9 etc shares ( linear and Pareto imputations)  Look at the types of income and deductions used by those in P99.

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Figure 8: Top Income Shares Using CRA data

Source: CANSIM , CRA, and CTaCS

Milligan and Smart: High Income Taxation

Table 9: Income Composition

150+ 250+ Linear Pareto Linear Pareto Category Category P99 P99 P99.9 P99.9 (1) (2) (3) (4) (5) (6) CANSIM Total 0.562* 0.562* 1.041** 1.041** Income [0.322] [0.322] [0.482] [0.482] Total income 0.943*** 1.321*** 0.670*** 0.983*** 0.452*** 1.559*** [0.310] [0.340] [0.255] [0.258] [0.167] [0.456] Total Employment 0.929** 1.289*** 0.522* 0.984*** 0.45 1.534*** [0.366] [0.400] [0.292] [0.329] [0.290] [0.567] Total Self- Employment 0.624 0.583 0.0421 0.351

• 0.272

0.889 [0.800] [1.354] [0.706] [0.911] [0.930] [0.928] Total Capital income 0.815 1.216*** 0.689* 0.882** 0.189 1.396*** [0.514] [0.450] [0.399] [0.419] [0.251] [0.505] Taxable Dividends 1.213* 1.850** 1.022 1.957** 0.734 2.110** [0.670] [0.775] [0.699] [0.994] [0.680] [0.967] Interest and Investment 0.241 0.469 0.0142 0.177

• 0.38

0.69 Income [0.386] [0.449] [0.504] [0.453] [0.386] [0.513] Capital Gains

• 0.547

0.251

• 0.756
• 1.235
• 0.653

0.685 [0.968] [0.386] [0.979] [1.777] [0.643] [0.714] Taxable income 1.151*** 1.507*** 0.850*** 1.196*** 0.628*** 1.734*** [0.261] [0.348] [0.170] [0.177] [0.115] [0.400] Total Deductions 0.227 0.612 0.0719 0.204

• 0.239

0.876 [0.711] [0.646] [0.619] [0.738] [0.640] [0.853] Total non-refundable 0.363 0.475

• 0.283

0.201

• 0.337**

0.72 Credits [0.353] [0.478] [0.276] [0.251] [0.169] [0.458] Charitable donations 0.505 0.576 0.363 0.367

• 0.251

0.868* [0.463] [0.604] [0.307] [0.464] [0.380] [0.518] RPP contributions

• 0.00692

1.740***

• 0.737

0.517 0.879 2.135** [0.498] [0.648] [0.541] [0.653] [0.864] [0.936] RRSP contributions 0.633 0.47 0.203 0.533**

• 0.375**

0.735* [0.395] [0.362] [0.136] [0.247] [0.176] [0.385] Union / Professional Dues 0.719 2.577** 0.161 1.661*** 1.413** 2.720*** [0.874] [1.095] [0.378] [0.437] [0.660] [0.645] Personal amounts 0.342 0.685**

• 0.226

0.394**

• 0.133

0.959*** [0.235] [0.318] [0.174] [0.175] [0.114] [0.333]

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Conclusion

We have three major findings

• 1. Top income elasticities appear to be very high.
• 2. Taxes not a major determinant of the high income ‘surge’
• 3. Hints that income shifting may be playing a role

Next steps: Look more deeply into Alberta’s role as an ‘internal tax haven’.

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