Estimating the Distribution of Consumption-based Taxes with the - - PowerPoint PPT Presentation

Estimating the Distribution of Consumption-based Taxes with the Consumption based Taxes with the Consumer Expenditure Survey

Ed Harris Ed Harris Kevin Perese Congressional Budget Office Tax Analysis Division

Disclaimer Disclaimer

Analysis and conclusions presented here are my own and should not be interpreted are my own and should not be interpreted as those of the Congressional Budget Office Office.

Uses of the CE Uses of the CE

• Primary Use: Distribution of consumption-

Primary Use: Distribution of consumption based taxes

– Excise taxes – Cap and Trade – Value-Added Tax?

• Estimated distributional effect of these

taxes depends critically on relationship y between consumption and income

• bserved in the CE

Average Excise Tax Rate By Income Quintile

3.0 Percent of Income 2 0 2.5

Lowest

1.5 2.0 0.5 1.0

Middle Highest

0.0

1979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 1997 998 999 000 001 002 003 004 005 006 1 19 1 19 19 19 19 19 1 19 19 19 1 19 19 1 19 19 1 19 19 20 2 20 20 20 20 20

Average Gain or Loss in Households' Purchasing Power from the Greenhouse-Gas Cap-and-Trade Program in H.R. 2454: 2020 Policy Measured at 2010 Levels of Income Income

Loss From Compliance Costs Gain From Allowance Allocations and Other Transfers Net Gain or Loss in Household Purchasing Power Average Dollar Gain or Loss per Household Lowest Quintile ‐430 555 125 Second Quintile ‐560 410 ‐150 Middle Quintile ‐685 375 ‐310 Fourth Quintile ‐825 455 ‐375 Highest Quintile ‐1,400 1,235 ‐165 Unallocated ‐120 130 10 All Households 900 740 160 All Households ‐900 740 ‐160 Gain or Loss as a Percentage of After‐Tax Income Lowest Quintile

• 2.5

3.2 0.7 Second Quintile

• 1.5

1.1

• 0.4

Middle Quintile

• 1.3

0.7

• 0.6

Fourth Quintile

• 1.1

0.6

• 0.5

Highest Quintile

• 0.7

0.6

• 0.1

Unallocated 0 2 0 2 0 0 Unallocated

• 0.2

0.2 0.0 All Households

• 1.2

1.0

• 0.2

Source: Congressional Budget Office, "The Economic Effects of Legislation to Reduce Greenhouse Gas Emissions", September 2009, Table2

Approach To Cap and Trade Estimates

• Estimate price effect of the cap and trade

Estimate price effect of the cap and trade program on final goods

• Impute expenditures by category from the
• Impute expenditures by category from the

CE to CBO’s base distributional database (which is based on income tax records (which is based on income tax records supplemented with data from the CPS) A l i ff t t di t ti t

• Apply price effect to spending to estimate

the effect across income groups

Input-Output Model: Price Change Results

Food 0.5% Clothing 0 2% Clothing 0.2% Nondurables 0.4% Electricity 8 8% Electricity 8.8% Natural Gas 11.4% Gasoline 4 2% Gasoline 4.2% All Expenditures 0.7%

Assumes Total allowance revenues of about 0.7% of GDP

CE and NIPA aggregates CE and NIPA aggregates

• CE aggregates generally below NIPA

CE aggregates generally below NIPA aggregates

• Applying price increases from NIPA based
• Applying price increases from NIPA based

I/O model to spending in the CE does not yield the same revenue yield the same revenue

• Differential across expenditure categories
• Adjusting for these has distributional

implications

Imputing Consumption: Preparing the CE

• We convert quarterly cross-sections from the

q y Interview Survey to annual panel files

– Reweight complete and incomplete interviews

Adj t t f di di

• Adjustments for diary spending
• Adjustments for renters with no reported

utility spending utility spending

• Pool multiple panels
• Two Methods to impute from adjusted CE:

p j

– Hot deck imputation for most of sample – Regression imputation for high income households households

Imputing Consumption: Statistical Match SOI/CPS & CE

• Hot deck routine with both rigid and flexible matching

i i criteria

– Fixed: Region – Flexible: Age (+/- 1 year increments) Income (+/- 2% increments) Family Type (+/- 1 child only)

• For each record in base data file, match to a CE

, record within the same cell

• Carry over ratio of consumption to income,

expenditure shares of different items e pe d tu e s a es o d e e t te s

• Applied to:
• Single households <$150,000 income
• Married households <$300,000 income

a ed

• use o ds

$300,000 co e

Consumption to Income ratios, 2004

BLS Published Income and Consumption by Income Class, 2004 BLS Published Income and Consumption by Income Class, 2004 Population Average Income Average Consumption Consumption/ Income < $5,000 4.553 $2,626 $17,029 6.49 < $10,000 7.218 $7,856 $14,596 1.86 < $15,000 8.950 $12,554 $19,444 1.55 < $20,000 8.177 $17,427 $23,023 1.32 , , , < $30,000 14.172 $24,892 $27,741 1.11 < $40,000 13.125 $35,107 $33,273 0.95 < $50,000 11.374 $45,052 $38,204 0.85 < $70,000 18.069 $59,920 $47,750 0.80 > $70,000 30.644 $118,332 $76,954 0.65 Total 116.282 $54,680 $43,395 0.79 NOTE: BLS consumption concept does NOT equal CBO consumption concept Consumption and income data are constructed based on b th d di d t both survey and diary data.

Source: BLS, Consumer Expenditure Survey, 2004 Table 2. Income before taxes: Average annual expenditures and characteristics.

Consumption-to-Income Ratios by Pre-tax-Income Quintiles, CEX 1994 - 2004

250% 300%

Quintile 1

200% 250% 150%

Quintile 2

100%

Quintile 3 Quintile 4

50%

Quintile 5

0% 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Source: BLS, Consumer Expenditure Survey, Table 1. Quintiles of income before taxes: Average annual expenditures and characteristics, multiple years

Potential Adjustments That Reduce C-I ratios at the Bottom

Adjustments Made: D l i d

• Drop very low-income records
• Use income averaged between 1st and last interview
• Estimate income taxes based on reported income, use ratio of

consumption to after-tax income consumption to after tax income

• Adjustments to consumption definition

Explored But Not Done: p

• Limit to prime-age individuals
• Cap consumption-income ratios unless observed dis-saving can

explain

• Even with these adjustments, C-I ratios are quite high for bottom of

the distribution

• Any adjustments to hit PCE totals exacerbate this problem

Any adjustments to hit PCE totals exacerbate this problem

High Income Regressions High Income Regressions

• Both income and expenditure amounts are

p top coded in CE

• Impute expenditure amounts based on

regression models for high income regression models for high-income households

• Need to extend analysis significantly beyond

Need to extend analysis significantly beyond the income range covered in the CE

• Separate models for electricity, gasoline, fuel

il t l d t t l dit

• il, natural gas, and total expenditures
• Use regression results up to 1M in income,

after that hold C-I ratio constant after that hold C I ratio constant

Comparison of High Income Units Comparison of High Income Units

CE SOI CE SOI Units above 100,000 Number of Units (M) 18.9 16.1 ( ) Average Income $164,000 $254,000 Share of Income 43.2 51.2 b Units above 150,000 Number of Units (M) 7.3 7.1 Average Income $236,000 $425,000 Share of Income 23 8 37 7 Share of Income 23.8 37.7

Source: BLS Table 2301. Higher income before taxes: Average annual expenditures and characteristics, Consumer Expenditure Survey, 2006 and IRS Statistics of Income, Individual Income Tax Returns 2006 Table 1.2

Top Quintile Income and Consumption Shares Top Quintile Income and Consumption Shares

0.6 0 5 0.55 0.45 0.5 CE Income CE Spending 0.35 0.4 CPS Income CBO Income 0.3

High Income Regressions High Income Regressions

ln(Consumption) by ln(Pre-tax-income), CEX 2004

$13 $14 $15 $10 $11 $12 umption) $7 $8 $9 $10 ln(Consu $5 $6 $7 10 10.5 11 11.5 12 12.5 13 ln(pre-tax-income)

$60,000 $160,000

High Income Regressions Projections

Ln(Consumption) = Ln(Pre-tax-Income)

$700,000 $800,000 70% 80% $400 000 $500,000 $600,000

• nsumption

40% 50% 60% tion-to-Income Ratio Predicted Consumption (Left Axis) $200,000 $300,000 $400,000 Predicted C 20% 30% 40% Predicted Consumpt Predicted Consumption-to-Income Ratio $0 $100,000 $500,000 $1,000,000 $1,500,000 $2,000,000 $2,500,000 0% 10% Predicted Consumption to Income Ratio (Right Axis) Pre-tax-income

High Income Regressions Effect of Top-coding

ln(Expenditures) = ln(Pre‐tax Income) S C O i

50% 60% 50% 60%

BLS vs. CBO estimates

40% 40%

• me Ratio

20% 30% 20% 30% Expenditures‐to‐Inco

BLS CBO

10% 10%

CBO

0% 0% $500,000 $1,000,000 $1,500,000 $2,000,000 $2,500,000 Pre‐tax Income

High Income Regressions Effect of Top-coding

ln(Gasoline Expenditures) = ln(Pre‐tax Income) BLS CBO i

1.80% 2.00% 1.80% 2.00%

BLS vs. CBO estimates

1.20% 1.40% 1.60% 1.20% 1.40% 1.60% come Ratio 0.60% 0.80% 1.00% 0.60% 0.80% 1.00% Expenditures‐to‐Inc 0.20% 0.40% 0.20% 0.40%

BLS CBO

0.00% 0.00% $500,000 $1,000,000 $1,500,000 $2,000,000 $2,500,000 Pre‐tax Income

Final Consumption-Income Ratio Final Consumption Income Ratio

0.2 1.8 1 2 1.4 1.6 0.1 0.8 1 1.2 Total Consumption (left scale) 0.2 0.4 0.6 Energy Consumption (right scale)

Lowest Quintile Second Quintile Middle Quintile Fourth Quintile Highest Quintile

(right scale)

Quintile Quintile Quintile Quintile Quintile

Evaluation Evaluation

• Data from CE is very valuable. Especially access

y p y to the micro data. Only source for:

– Detailed consumption V i ti f ti b hi i – Variation of consumption by age, geographic region,

• But
• But…

– Observed consumption-income pattern is difficult to explain – Differential reporting error across income groups – Raises questions about the expenditure shares derived from the CE derived from the CE

Suggested Improvements Suggested Improvements

Major

• Top-down reconciliation of income and consumption as part of

the interview process

– Perhaps something like to the diary, where focus is total spending/saving spending/saving

• High-Income oversample

Minor

– Pool all interviews for a CU create panel weights – Pool all interviews for a CU, create panel weights – Impute from diary to interview, so one complete file – Continue research into reconciling differences with PCE

• Provide cross-walk (or adjustment factors) between NIPA PCE and

( j ) UCC codes

– Study income misreporting with a one-time match to administrative records