to Uncover the Impacts of Income Taxation on Earnings Raj Chetty, - - PowerPoint PPT Presentation

Using Differences in Knowledge Across Neighborhoods to Uncover the Impacts of Income Taxation on Earnings

Raj Chetty, Harvard and NBER John N. Friedman, Harvard and NBER Emmanuel Saez, UC Berkeley and NBER December 2011

Identifying Policy Impacts

Two central challenges in identifying the impacts of tax policies:

• 1. Difficult to find comparison groups to estimate causal impacts of

policies [Meyer 1995, Gruber 2008]

• 2. Difficult to identify long run impacts from short-run responses

to tax changes Many people are uninformed about tax and transfer policies

[Brown 1968, Bises 1990, Chetty and Saez 2009]

Workers face switching costs for labor supply

[Cogan 1981, Altonji and Paxson 1992, Chetty et al. 2011]

Overview

We address these challenges by exploiting differences across neighborhoods in knowledge about tax policies Idea: use cities with low levels of information about tax policies as “control groups” for behavior in the absence of tax policy Apply this approach to characterize the impacts of the Earned Income Tax Credit (EITC) on the earnings distribution in the U.S. EITC provides refunds of up to $5,000 to approximately 20 million households in the U.S.

$0 1 2 3 4

Earned Income Tax Credit Schedule for Single Earners with One Child

$10K $20K $30K

EITC Credit Amount ($1000) Taxable Income

$0 0% 1% 2% 3% 4%

Income Distribution for Single Wage Earners with One Child Percent of Wage-Earners

$10K $20K $30K $0 $2K

EITC Credit Amount

$1K $3K $4K

W-2 Wage Earnings

$0 0% 1% 2% 3% 4%

Income Distribution for Single Wage Earners with One Child Percent of Wage-Earners

$10K $20K $30K $0 $2K

EITC Credit Amount

$1K $3K $4K

W-2 Wage Earnings Is the EITC having an effect on this distribution?

Outline

• 1. Data and Institutional Background
• 2. A Proxy for Knowledge: Sharp Bunching via Income Manipulation
• 3. Using Neighborhood Effects to Uncover Wage Earnings Responses
• 4. Implications for Tax Policy

Selected data from population of U.S. income tax returns, 1996-2009 Includes 1040’s and all information forms (e.g. W-2’s) For non-filers, we impute income and ZIP from W-2’s For joint filers, code income as total household income or W-2’s Sample restriction: individuals who at least once between 1996-2009: (1) file a tax return, (2) have income < $40,000, (3) claim a dependent Sample size after restrictions: 77.6 million unique taxpayers 1.09 billion taxpayer-year observations on income

Data and Sample Definition

Variable Mean Income $21,175 Self Employed 9.1% Married 24% Number of Children 0.78 Female (among single filers) 58%

Summary Statistics

Critical distinction: wage earnings vs. self-employment income Self employed = filers with any Schedule C income Wage earners = filers with no Schedule C income Self-employment income is self-reported  easy to manipulate Wage earnings are directly reported to IRS by employers Therefore more likely to reflect “real” earnings behavior Analyze misreporting due to EITC using National Research Program Tax Audit data (joint with Peter Ganong, Kara Leibel, and Alan Plumley)

Self Employment Income vs. Wage Earnings

$0 $1K

EITC Credit

$0 $10K $20K $30K $40K

Taxable Income (Real 2010 $) Two children One child

$2K $3K $4K $5K

2008 Federal EITC Schedule for a Single Filer with Children

0% 1% 2% 3% 4% 5%

Income Distributions for Individuals with Children in 2008 Percent of Individuals

$0 $10K $20K $30K $40K

Taxable Income (Real 2010 $) Two children One child

0% 5% 10% 15% Reported vs. Audited Income Distributions for SE EITC Filers in 2001 National Research Program Tax Audit Data

• $10K

$0 $10K $20K $30K Reported Income Percent of Filers

Source: IRS TY01 NRP reporting compliance study of individual income tax returns for those reporting dependent children; amounts reflect only what was detected by the auditors, weighted to population levels.

Income Relative to First Kink of EITC Schedule

0% 5% 10% 15%

• $10K

$0 $10K $20K $30K Reported Income Detected Income Percent of Filers

Source: IRS TY01 NRP reporting compliance study of individual income tax returns for those reporting dependent children; amounts reflect only what was detected by the auditors, weighted to population levels.

Taxable Income Relative to First Kink of EITC Schedule Reported vs. Audited Income Distributions for SE EITC Filers in 2001 National Research Program Tax Audit Data

Income Relative to Kink Percent of Filers Reported vs. Audited Income Distributions for EITC Wage Earners with Children National Research Program Tax Audit Data 0% 2% 4% 6%

• $10K

$0 $10K $20K $30K Reported Income Detected Income Taxable Income Relative to First Kink of EITC Schedule

Source: IRS TY01 NRP reporting compliance study of individual income tax returns for those reporting dependent children; amounts reflect only what was detected by the auditors, weighted to population levels.

Outline of Empirical Analysis

Step 1: Develop a proxy for knowledge about the EITC in each neighborhood using sharp bunching among self-employed

Income Relative to 1st Kink Income Distribution in Texas for the Self-Employed

0% 5% 10% 15%

• $10K

$0 $10K $20K

Percent of Self-Employed Filers

0% 5% 10% 15%

• $10K

$0 $10K $20K

Income Distribution in Kansas for the Self-Employed Percent of Self-Employed Filers Income Relative to 1st Kink

Self-employed sharp bunching Fraction of EITC-eligible tax filers who report income at first kink and have self-employment income Essentially measures fraction of individuals who manipulate reported income to maximize EITC refund in each neighborhood Begin by examining spatial evolution of sharp self-employed bunching across the United States

Neighborhood-Level Measure of Bunching

Self-Employed Sharp Bunching in 1996

3.5 – 7.0% 2.5 – 3.5% 2.1 – 2.5% 1.8 – 2.1% 1.6 – 1.8% 1.4 – 1.6% 1.3 – 1.4% 1.0– 1.3% 0 – 1.0%

Self-Employed Sharp Bunching in 1999

3.5 – 7.0% 2.5 – 3.5% 2.1 – 2.5% 1.8 – 2.1% 1.6 – 1.8% 1.4 – 1.6% 1.3 – 1.4% 1.0– 1.3% 0 – 1.0%

Self-Employed Sharp Bunching in 2002

3.5 – 7.0% 2.5 – 3.5% 2.1 – 2.5% 1.8 – 2.1% 1.6 – 1.8% 1.4 – 1.6% 1.3 – 1.4% 1.0– 1.3% 0 – 1.0%

Self-Employed Sharp Bunching in 2005

3.5 – 7.0% 2.5 – 3.5% 2.1 – 2.5% 1.8 – 2.1% 1.6 – 1.8% 1.4 – 1.6% 1.3 – 1.4% 1.0– 1.3% 0 – 1.0%

Self-Employed Sharp Bunching in 2008

3.5 – 7.0% 2.5 – 3.5% 2.1 – 2.5% 1.8 – 2.1% 1.6 – 1.8% 1.4 – 1.6% 1.3 – 1.4% 1.0– 1.3% 0 – 1.0%

Self-Employed Sharp Bunching in 2008 by 3-Digit Zip Code in Kansas, Louisiana, Oklahoma, and Texas

0.0121 – 0.0510 0.0091 – 0.0121 0.0072 – 0.0091 0.0062 – 0.0072 0.0053 – 0.0062 0.0047 – 0.0053 0.0041 – 0.0047 0.0035 – 0.0041 0 - 0.0035

Austin San Antonio

0% 2% 4% 6% 8%

• $10K

$0K $10K $20K $30K

Income Distributions in Lowest vs. Highest Decile Neighborhoods Percent of Individuals Income Relative to First EITC Kink Highest Information Neighborhoods Lowest Information Neighborhoods

Outline of Empirical Analysis

Step 1: Develop a proxy for knowledge about the EITC in each neighborhood using sharp bunching among self-employed Step 2: Establish learning as a mechanism for differences in sharp bunching across neighborhoods

Look at individuals who move across neighborhoods to isolate causal impacts of neighborhoods on elasticities 54 million observations in panel data on cross-zip movers Define “neighborhood sharp bunching” as degree of bunching for stayers Classify movers based on deciles of neighborhood response of original neighborhood and new neighborhood

Movers: Neighborhood Changes

• 5

5 Event Year Event Study of Bunching for Movers, by Destination Area 0.0% 0.4% 0.8% 1.2% Self-Emp. Sharp Bunching for Movers De = 0.41% (0.05%)

Movers to Lowest Information Areas Movers to Medium Information Areas Movers to Highest Information Areas

Percent of Movers

• $10K

$0 $10K $20K $30K

Income Relative to 1st Kink

1% 2% 3% 4% 5%

Movers’ Income Distributions: Before Move

Movers to Lowest Information Areas Movers to Medium Information Areas Movers to Highest Information Areas

1% 2% 3% 4% 5%

• $10K

$0 $10K $20K $30K

Income Relative to 1st Kink Percent of Movers Movers’ Income Distributions: After Move

Movers to Lowest Information Areas Movers to Medium Information Areas Movers to Highest Information Areas

Knowledge model makes strong prediction about asymmetry of effects: Memory: level of response in prior neighborhood should continue to matter for those who move to a low-EITC-response neighborhood Learning: prior neighborhood matters less when moving to a high- EITC-response neighborhood

Learning and Asymmetry

Post-Move Distributions for Movers to Lowest-Information Neighborhoods 0% 1% 2% 3% 4% Percent of Movers

• $10K

$0 $10K $20K $30K Income Relative to 1st Kink

Movers from Lowest Information Areas Movers from Medium Information Areas Movers from Highest Information Areas

 Memory: old neighborhood matters when moving to lowes est- t-inf inform rmatio tion areas

Post-Move Distributions for Movers to Highest-Information Neighborhoods

• $10K

$0 $10K $20K $30K 0% 2% 4% 6% 8% Percent of Movers Income Relative to 1st Kink

Movers from Lowest Information Areas Movers from Medium Information Areas Movers from Highest Information Areas  Learning: Old neighborhood does not matter

when moving to high ghes est-info form rmat atio ion areas

Dependent variable: b for movers Move Up Move Down (1) (2)

bold

0.252 0.496 (0.058) (0.046)

bnew

0.822 0.354 (0.058) (0.046) Asymmetric Impact of Neighborhoods on Bunching

p Value for Relative Change in Coefficients Across Columns: p < 0.001

bmover    old b

neighborhood

• ld

 new b

neighborhood new

.01 .015 .02 .025 .03 .035 .05 .1 .15 .2

Agglomeration: Sharp Bunching vs. Fraction EITC Filers by ZIP Code Fraction EITC Filers in 3-Digit ZIP Code Sharp Bunching

1995 2000 2005 2010 Below-Median EITC Density Above-Median EITC Density 2.5% 2.0% 1.0% 1.5%

Year Sharp Bunching Evolution of Sharp Bunching in Low vs. High EITC-Density Neighborhoods

2.5% .4 .5 .6 .7 .8

Fraction of Professionally Prepared Returns in 3-Digit Zip Code

2.0% 1.0% 1.5% 0.5%

Correlation of Sharp Bunching with Professional Tax Preparation in Neighborhood Sharp Bunching

Sharp Bunching

Among Self-Prepared Among Professionally Prepared .4 .5 .6 .7 .8 2.5% 2.0% 1.0% 1.5% 0.5%

Fraction of Professionally Prepared Returns in 3-Digit Zip Code Correlation of Sharp Bunching with Professional Tax Preparation in Neighborhood Self-Prepared vs. Professionally-Prepared Filers

Outline of Empirical Analysis

Step 1: Develop a proxy for knowledge about the EITC in each neighborhood using sharp bunching among self-employed Step 2: Establish learning as a mechanism for differences in sharp bunching across neighborhoods Step 3: Compare wage earnings distributions across low- and high- knowledge neighborhoods to uncover impacts of EITC on earnings

$0 $10K $20K $30K 0% 1% 2% 3% 4% W-2 Earnings Distributions for Wage Earners with One Child Percent of Wage-Earners Is the EITC having an effect on this distribution? Income

$0 $10K $20K $30K

Low Information Neighborhoods High Information Neighborhoods

Income 0% 1% 2% 3% 4% Percent of Wage-Earners W-2 Earnings Distributions in High vs. Low Information Areas Wage Earners with One Child

$0 $1000 $2000 $3000 $4000 $5000 EITC Amount

• .01
• .005

.005 .01

• $10K

$0 $10K $20K $30K W-2 Earnings Distributions in High vs. Low Information Areas Wage Earners with Two Children Income Relative to First Kink in EITC Schedule All Firms Difference in Income Densities

$0 $1000 $2000 $3000 $4000 $5000 EITC Amount

• .01
• .005

.005 .01

• $10K

$0 $10K $20K $30K >100 Employees All Firms Difference in Income Densities W-2 Earnings Distributions in High vs. Low Information Areas Wage Earners with Two Children Income Relative to First Kink in EITC Schedule

EITC Credit Amount for Wage Earners EITC Credit Amount for Wage Earners with Two Children

• vs. Neighborhood Self-Employed Sharp Bunching

$3200 $3250 $3300 $3350 0.0% 0.8% 1.6% 2.4% 3.2% 4.0%

Neighborhood Self-Emp. Sharp Bunching

Outline of Empirical Analysis

Step 1: Develop a proxy for knowledge about the EITC in each neighborhood using sharp bunching among self-employed Step 2: Establish learning as a mechanism for differences in sharp bunching across neighborhoods Step 3: Compare wage earnings distributions across low- and high- knowledge neighborhoods to uncover impacts of EITC on earnings Step 4: Compare impacts changes in EITC subsidies on earnings across low

• vs. high knowledge nbhds. to account for omitted variables

Cross-sectional differences in income distributions could be biased by

• mitted variables

City effects: differences in industry structure or labor demand Individual sorting: preferences may vary across cities We account for these omitted variables by analyzing impacts of changes in EITC subsidy Do EITC changes affect earnings more in high knowledge cities?

Accounting for Omitted Variables: Tax Changes

To identify causal impacts of EITC, need variation in tax incentives Birth of first child  substantial change in EITC incentives Although birth affects labor supply directly, cross-neighborhood comparisons provide good counterfactuals 12 million EITC-eligible individuals give birth within our sample

Child Birth as a Source of Tax Variation

3% 4% 5% 6% 7% 8% 9%

Earnings Distributions in the Year Before First Child Birth for Wage Earners Percent of Individuals

$0 $10K $20K $30K $40K Lowest Information Neighborhoods Medium Information Neighborhoods Highest Information Neighborhoods

Income

3% 4% 5% 6% 7% 8% 9% $0 $10K $20K $30K $40K

Income Earnings Distributions in the Year of First Child Birth for Wage Earners Percent of Individuals

Lowest Information Neighborhoods Medium Information Neighborhoods Highest Information Neighborhoods

3% 4% 5% 6% 7% 8% 9%

Earnings Distributions in the Year of First Child Birth for Wage Earners Individuals Working at Firms with More than 100 Employees Percent of Individuals

$0 $10K $20K $30K $40K

Income

Lowest Information Neighborhoods Medium Information Neighborhoods Highest Information Neighborhoods

$900 $1000 $1100 $1200 $1300 $1400

• 4
• 2

2 4

Simulated EITC Credit Age of Child Simulated EITC Credit Amount for Wage Earners Around First Child Birth Individuals Working at Firms with More than 100 Employees

Lowest Information Neighborhoods Medium Information Neighborhoods Highest Information Neighborhoods

.5 1 1.5

• 4
• 2

2 4

Age of Child

Lowest Information Neighborhoods Medium Information Neighborhoods Highest Information Neighborhoods

Number of EITC Qualifying Children Number of EITC Qualifying Children Claimed Around Birth of 1st Child

Where is the increase in EITC refunds coming from? Phase-in, phase-out, or extensive margin? Important for understanding welfare consequences of EITC Calculate increase in EITC amounts from year -1 to 0 relative to baseline change from -2 to -1 Compare across low and high information areas to recover causal impact of EITC

Composition of Wage Earnings Responses

• $100

$0 $100 $200 0% 2% 4% 6% 8%

β = 32.6 (2.98) Change in Simulated EITC Credit Changes in Simulated EITC Credit around Births for Wage Earners 0 to 1 Child Neighborhood Self-Emp. Sharp Bunching

• $100

$0 $100 $200 0% 2% 4% 6% 8%

β = -0.298 (1.61) β = 32.6 (2.98) Change in Simulated EITC Credit Changes in Simulated EITC Credit around Births for Wage Earners 0 to 1 Child 2 to 3 Children Neighborhood Self-Emp. Sharp Bunching

• $200
• $100

$0 $100 $200 0% 2% 4% 6% 8%

Neighborhood Self-Emp. Sharp Bunching Change in Simulated EITC Credit Changes in Simulated EITC Credit around Births for Wage Earners β = 44.62 (3.46) Phase in

• $200
• $100

$0 $100 $200 0% 2% 4% 6% 8%

Phase out Neighborhood Self-Emp. Sharp Bunching Change in Simulated EITC Credit Changes in Simulated EITC Credit around Births for Wage Earners Phase in β = 10.89 (1.72) β = 44.62 (3.46)

0% 2% 4% 6% 8% 10%

Neighborhood Self-Emp. Sharp Bunching Change in Fraction Working Extensive Margin: Changes in Probability of Working around First Birth β = 1.46 (0.045)

0% 2% 4% 6% 8%

Assume that extensive margin entrants obtain average EITC refund of $1,300 Where is the increase in EITC refunds coming from? Phase-In: 46% Phase-Out: 13% Zero earnings (extensive margin): 23% Plateau: 18%

Composition of Wage Earnings Responses

Our estimates can be used to characterize impact of EITC on income distribution taking into account behavioral responses Use neighborhoods with little self-employment bunching as counterfactual for earnings distribution without EITC

Tax Policy Implications

0% 1% 2% 3% 4%

Percent of Wage-Earners Impact of EITC on Income Distribution for Single Earners with 2+ Children

No EITC Counterfactual

Total Income (including EITC refund)

$0 $10K $20K $30K $40K

0% 1% 2% 3% 4%

Percent of Wage-Earners

No EITC Counterfactual EITC, No Behavioral Response

Total Income (including EITC refund)

$0 $10K $20K $30K $40K

Impact of EITC on Income Distribution for Single Earners with 2+ Children

0% 1% 2% 3% 4%

Percent of Wage-Earners

No EITC Counterfactual EITC, No Behavioral Response EITC with Behavioral Response

Total Income (including EITC refund)

$0 $10K $20K $30K $40K

Impact of EITC on Income Distribution for Single Earners with 2+ Children

0% 1% 2% 3% 4%

Percent of Wage-Earners

No EITC Counterfactual EITC, No Behavioral Response EITC with Behavioral Response

Total Income (including EITC refund)

$0 $10K $20K $30K $40K

Impact of EITC on Income Distribution for Single Earners with 2+ Children

Povert verty y Rates es among

• ng EITC

C Clai aimant mants s with h 2 Kids ds No EITC: 48.1 .1% EITC, No Behavioral Response: 28.3 .3% EITC, with Behavioral Response: 24.7 .7%

Our estimates imply that average EITC refund amount for wage-earners is 7% 7% ($140) larger due to behavioral responses 40% of aggregate response from the top 10% of neighborhoods Response primarily due to an intensive-margin increase in earnings coming from the phase-in region and extensive margin In neoclassical model, generating an increase of 7% in refund amount would require an intensive margin taxable income elasticity of 0.2

Tax Policy Implications

Neighborhood effects could be used to uncover impacts of many policies Example: Saver’s Credit Saver’s Credit provides up to a 100% subsidy to save in an IRA for low-income households Eligibility based on discontinuous income thresholds Previous work has documented modest impacts of saver’s credit on IRA contributions in aggregate [Duflo et al. 2006, 2007; Ramnath 2011]

Neighborhood Effects: Other Applications

1.6% 1.8% 2.0% 2.2% 2.4% 2.6% 2.8% 3.0%

• $5K
• $4K
• $3K
• $2K
• $1K

$0 $1K $2K $3K $4K $5K IRA Take-Up Rates by Income Bin Income % Take-up of IRA

0.813 -1.292 0.680 -0.813 0.593 -0.680 0.548 -0.593 0.505 -0.548 0.459 -0.505 0.435 -0.459 0.386 -0.435 0.184 -0.386

Savers Credit Response, 2002-2008

Saver’s Credit Response by 3-Digit Zip, 2002-2008 in Illinois, Indiana, Michigan, and Wisconsin

0.87 - 1.63 0.65 - 0.87 0.55 - 0.65

• 0.30 - 0.55

Detroit Chicago Indianapolis

0.028 -0.045 0.025 -0.028 0.024 -0.025 0.022 -0.024 0.020 -0.022 0.019 -0.020 0.017 -0.019 0.014 -0.017 0.012 -0.014

IRA Take-Up, 2002-2008

Substantial expenditure on Working for Families credit may not maximize positive impact on incentives to work Knowledge about tax code likely to diffuse more slowly in a less dense country Particularly high effective tax rates in abatement region (incomes around $70K) are a concern Large incentives could draw attention and generate adverse responses in a part of the income distribution with many workers Impacts of recent reduction in income tax rates likely to be beneficial but may take time to be realized Would be very valuable to study tax microdata to directly understand impacts of NZ’s tax system on earnings behavior

Implications for Tax Policy in New Zealand