Distributional National Accounts: Methods and
Distributional National Accounts: Methods and Estimates for the United States since 1913 Thomas Piketty (PSE) Emmanuel Saez (UC Berkeley) Gabriel Zucman (LSE) April 2015 There is a large disconnect today between the study of inequality and macro Macro: use national accounts, with no info on distribution Inequality: use survey & tax data, inconsistent with macro totals This gap makes it hard to know how growth is distributed: How does growth of bottom 90% compare to total growth? What fraction of growth accrues to top 1%? ⇓ We need answers to these questions to better understand interplay between growth, inequality, and gov. intervention Sources of the disconnect between inequality and macro Large gap between surveys and macro totals Conceptual differences: household income vs. national income Practical pbs: non-response & under-reporting at the top Some progress using tax data, but insufficient Atkinson, Alvaredo, Piketty, Saez and 30+ used tax data to construct top shares in 28+ countries, 1870-2012 → WTID But: tax data miss tax-exempt income and tax evasion Ex: in US only capture 60% of US national income Silent on post-tax and transfer income Silent on distribution within bottom 90% DINAs are a new tool to connect inequality and macro While today’s national accounts are spreadsheets with aggregates only, tomorrow they could be micro databases where: Each observation is a synthetic individual Distributions of income, wealth, taxes, transfers... are consistent with what survey/tax data show Totals match macro aggregates Creating DINAs involves: Defining a clear common conceptual framework Incorporating results from economic theory Developing statistical techniques to optimally use information from available micro sources Following up on King (1696) and Kuznets, with improved data and method Global DINAs WTID will be replaced by a World Wealth and Income Database (W2ID) Ultimate goal is to enable governments to publish their own DINAs all over the world Gov. agencies able to deploy large teams to refine estimates Same process as for today’s NA 1930s-40s: prototypes developed by Kuznets, Kendrick, Meade, Stone → improved by agencies 1950s-2015 This paper Goal: construct micro database of income, taxes and transfers consistent with NIPA totals in the United States By combining tax, survey, and national accounts data in a consistent manner Pre-tax & transfers vs. post-tax & transfers (same aggregate) ⇒ First estimates of income inequality covering 100% of national income ⇒ First decompositions of growth by groups consistent with growth used in macro Roadmap 1. The limits of current estimates of US income inequality 2. US DINA methodology: general principles 3. Methodology to capture 100% of pre-tax capital and labor income 4. The distribution of pre-tax national income since 1913 5. The interplay between income and wealth inequality The limits of current estimates of US income inequality Goal: allocate total US national income Y = YK + YL to the adult pop. Composition of national income in the United States since 1913 100% 80% 70% Labor income 60% 50% 40% 30% 20% 2013 2008 2003 1998 1993 1988 1983 1978 1973 1968 1963 1958 1953 1943 1938 1933 1928 1923 1918 0% 1948 Capital income 10% 1913 % of factor-price national income 90% Most capital income is missed by tax data From tax-reported to total capital income 35% Retained earnings % of factor-price national income 30% 25% 20% 15% 10% 5% Non-filers & unreported sole Corporate income tax prop. profits Income paid to pensions & insurance Imputed rents Didivends, interest, rents & profits reported on tax returns 0% 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 Most capital income is missed by tax data From tax-reported to total capital income 35% Retained earnings % of factor-price national income 30% 25% 20% 15% 10% 5% 2/3 missed Non-filers & unreported sole Corporate income tax prop. profits by tax data Income paid to pensions & insurance Imputed rents Didivends, interest, rents & profits reported on tax returns 0% 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 A growing fraction of labor income is missed by tax data From taxable to total employee compensation % of total NIPA compensation of employees 100% 95% Other Employer payroll taxes 90% 85% Pension contributions 80% Health benefits 75% 70% 65% 60% Reported taxable wages 55% 50% 1953 1958 1963 1968 1973 1978 1983 1988 1993 1998 2003 2008 2013 A growing fraction of labor income is missed by tax data From taxable to total employee compensation % of total NIPA compensation of employees 100% 95% Other Employer payroll taxes 90% 85% 1/4 missedPension by tax data contributions 80% Health benefits 75% 70% 65% 60% Reported taxable wages 55% 50% 1953 1958 1963 1968 1973 1978 1983 1988 1993 1998 2003 2008 2013 There is much less growth in tax data than in the national accounts Average real income: national accounts vs. tax data (1946 = 100) National income per adult 300 260 220 180 IRS market income per family (Piketty-Saez, CPI) 140 Source: Appendix Tables XX. 2010 2006 2002 1998 1994 1990 1986 1982 1978 1974 1970 1966 1962 1958 1954 1950 1946 100 US DINA methodology: General principles Income concept and unit of observation Income concept: US (pre-tax) national income: Only adjustment: remove investment income of nonprofits (≈ 2% of national income) Unit of observation: Adult [individual over 20] Preferable to family (fewer marriage → less growth) and individual (higher fertility → less growth) Deflator: National income deflator Preferable to CPI (overstates inf.) and PCE deflator (too narrow) Reconciling growth in tax data and national accounts Average real income: national accounts vs. tax data (1946 = 100) National income per adult 300 IRS market income per adult (national income deflator) 260 IRS market income per family (national income deflator) 220 180 IRS market income per family (Piketty-Saez, CPI) 140 Source: Appendix Tables XX. 2010 2006 2002 1998 1994 1990 1986 1982 1978 1974 1970 1966 1962 1958 1954 1950 1946 100 How we allocate taxes We follow standard tax incidence results: Labor taxes fall on labor; capital taxes on capital (and corporate tax on all capital assets: Harberger 1962) Reasonable if Y = F (K , L) has elasticity of substitution σ >> labor supply elasticity eL and capital supply elasticity eK Cross-country and time-series evolution of α = rK /Y and β = K /Y broadly consistent with view that σ > 1 and eL and eK relatively small in the long run σ eK But this is uncertain → will revisit if needed Methodology: Distributing pre-tax capital and labor income How we construct micro-measures of capital income matching macro totals 1. Construct micro-measures of wealth Follow Saez and Zucman 2014: category by category, pragmatic approach Delivers micro-measures of family wealth matching macro totals Split capital 50/50 among spouses 2. Derive micro-measures of pre-tax capital income Compute aggregate pre-tax rates of return for each asset class that reconcile NIPA income flows with Flow of Funds wealth Apply these rates of return to individual assets How we construct distributional estimates of national labor income 1. Start from reported wages; split income of spouses using W2 forms 2. Employer payroll taxes: apply schedule, e.g., in 2015: Medicare: 1.45% Social Security: 7.2% capped at $118,500 Unemployment insurance: vary by state 3. Pension and health insurance contributions: Available on W2 forms since 1999 for pensions, since 2012 for health Before, assume same distribution The distribution of pre-tax income since 1913 National income is more concentrated than tax income Top 10% pre-tax income shares 55% 45% National income per adult (DINA) 40% 35% 30% 2012 2007 2002 1997 1992 1987 1982 1977 1972 1967 1962 1957 1952 1947 1942 1937 1932 1927 1922 25% IRS family market income (Piketty-Saez) 1917 % of total income 50% This figure displays the share of total pre-tax national income earned by top 10% adult income earners and the share of total IRS market income earned by top 10% family tax units. Source: Appendix Tables XX. Less true for top 1% Share of national income earned by top 1% adult income earners 25% National income per adult (DINA) % of total income 20% 15% 10% This figure displays the share of total pre-tax national income earned by top 1% adult income earners and the share of total IRS market income earned by top 1% tax units. Source: Appendix Tables XX. 2013 2008 2003 1998 1993 1988 1983 1978 1973 1968 1963 1958 1953 1948 1943 1938 1928 1923 1918 0% 1913 5% 1933 IRS family market income (Piketty-Saez) Until late 1990s, rise in the top 1% driven by an upsurge in top wages Labor income of top 1% adult income earners 14% 10% 8% 6% Compensation of employees 4% 2% 2008 2003 1998 1993 1988 1983 1978 1973 1968 1963 1958 1953 1948 1943 1938 1933 1928 1923 1918 Labor income in noncorporate businesses 0% 1913 % of national income 12% ... But since late 1990s, top 1% rises because of capital income Capital income of top 1% adult income earners 14% 12% Profits & interest paid to pensions 8% 6% Corporate profits 4% Net interest 2% 2008 2003 1998 1993 1988 1983 1978 1973 1968 1963 1958 1953 1948 1943 1938 1933 1928 1923 1918 0% Noncorporate profits Housing rents 1913 % of national income 10% DINAs make it possible to compute growth rates consistent with macro totals Real average national income: Full adult population vs. bottom 90% All adults 60,000 1.4% 50,000 40,000 2.0% 30,000 20,000 Bottom 90% adults 0.7% 2.0% 10,000 2014 2010 2006 2002 1998 1994 1990 1986 1982 1978 1974 1970 1966 1962 1958 1954 1950 0 1946 Average income in constant 2012 dollars 70,000 Real values are obtained by using the national income deflator and expressed in 2012 dollars. Source: Appendix Tables XX. The top 10% has grown three times faster than the bottom 90% since 1980 300,000 70,000 60,000 250,000 50,000 2.3% 200,000 40,000 1.9% 150,000 30,000 Top 10% (left axis) 0.7% 100,000 Bottom 90% (right axis) 2.0% 50,000 20,000 10,000 2014 2010 2006 2002 1998 1994 1990 1986 1982 1978 1974 1970 1966 1962 1958 1954 0 1950 0 Bottom 90% real average national income Real average national income of bottom 90% and top 10% adults 1946 Top 10% real average national income 350,000 Real values are obtained by using the national income deflator and expressed in 2012 dollars. Source: Appendix Tables XX. Top 1% vs. bottom 90%: from a factor of 20 to a factor of 40 Real average national income of bottom 90% and top 1% adults Top 1% (left axis) 1,200,000 1,000,000 60,000 50,000 3.5% 800,000 40,000 600,000 30,000 400,000 0.7% Bottom 90% (right axis) 1.4% 200,000 20,000 10,000 2.0% 2014 2010 2006 2002 1998 1994 1990 1986 1982 1978 1974 1970 1966 1962 1958 1954 0 1950 0 Real values are obtained by using the national income deflator and expressed in 2012 dollars. Source: Appendix Tables XX. Bottom 90% real average national income 70,000 1946 Top 1% real average national income 1,400,000 The distribution of economic growth in the US since 1946 All Bottom 90% Bottom 99% Top 10% Top 1% Top 0.1% Top 0.01% Average yearly growth rates of real national income per adult 1.7% 1.4% 1.6% 2.1% 2.4% 3.0% 3.8% 1946-1980 1946-2012 2.0% 2.0% 2.1% 1.9% 1.4% 1.4% 1.8% 1980-2012 1.4% 0.7% 1.0% 2.3% 3.5% 4.7% 6.1% 2009-2012 2.0% 0.1% 0.8% 3.9% 7.0% 9.1% 9.5% Fraction of national income growth accruing to each groups 1946-2012 100% 43% 76% 57% 24% 12% 6% 1946-1980 100% 62% 92% 38% 8% 3% 1% 1980-2012 100% 27% 62% 73% 38% 20% 10% 2009-2012 100% 1% 32% 99% 68% 38% 18% The interplay between income and wealth inequality Synthetic saving rates by fractile How we compute saving rates by fractile: 1. Start from individual i wealth accumulation equation: i Wt+1 = (1 + qti ) · (Wti + sti · Yti ) where Wti is wealth, Yti is income, sti is net savings rate, 1 + qti is pure price effect on assets in year t 2. Consider similar equation for fractile p (e.g., top 1%): p Wt+1 = (1 + qtp ) · (Wtp + stp · Ytp ) 3. Combining information on composition of p’s wealth and economy-wide price effects by asset class, we can compute 1 + qtp 4. So we can infer from (2) the synthetic saving rate stp of fractile p, i.e., the flow of saving that reconciles p’s change in wealth btw t and t + 1 given change in price of assets held by p Saving rates rise with wealth Saving rates by wealth class (decennial averages) 45% Top 1% 35% 25% Top 10 to 1% 15% 5% Bottom 90% 2010-12 2000-09 1990-99 1980-89 1970-79 1960-69 1950-59 1940-49 1930-39 -15% 1920-29 -5% 1917-19 % of each group's total primary income 55% The average private (household + corporate) saving rate has been 11.4% over 1913-2013, but the rich save more as a fraction of their income, except in the 1930s when there was large dis-saving through corporations. Source: Appendix Table B33. Combination of rising income and saving rate inequality is fueling wealth inequality 140,000 12,000,000 120,000 Top 1% real average welath 14,000,000 10,000,000 100,000 8,000,000 80,000 6,000,000 Bottom 90% (right axis) 60,000 4,000,000 40,000 Real values are obtained by using the GDP deflator, 2010 dollars. Source: Appendix Tables B3. bottom90 2014 2010 2006 2002 1998 1994 1990 20,000 1986 1982 1978 1970 1966 1962 1958 1954 1950 1946 0 1974 Top 1% (left axis) 2,000,000 0 Bottom 90% real average wealth Real average wealth of bottom 90% and top 1% families Capital accumulated out of surging labor income generates a sizable return Yield and total return on U.S. private wealth 20% Pure yield = capital income (including retained earnings) / wealth 15% 10% -5% Total return = pure yield + asset price effect -10% back 2013 2008 2003 1998 1993 1988 1983 1978 1973 1968 1963 1958 1953 1948 1943 1938 1933 1928 1923 1918 0% 1913 5% The labor income share of top wealth holders has grown but is stabilizing Share of income earned by top 0.1% wealth-holders 8% % of total pre-tax income 7% 6% National income 5% 4% 3% 2% Labor income 1% 0% 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 This figure shows the share of total pre-tax national income and pre-tax labor income earned by top 0.1% wealth-holders. Labor income includes employee compensation and the labor component of business income. Source: Appendix Tables B25 and B28. back Conclusion The national accounts of the future The DINA agenda: Construct new series on the distribution of wealth, income, saving... fully consistent with macro aggregates Key tool: a new micro-dataset of synthetic observations coherent with distributional data and matching macro totals Preliminary, exploratory and uncertain, but conceptually the right way to go First US DINA results: Large rise in top national income shares Rise in income inequality since late 1990s due to capital, not labor DINAs for policymakers We will make our prototype DINAs publicly available on dedicated websites → everybody able to conduct own distributional analysis Useful for policy-makers: Simulate reforms of tax and transfer policies Monitor financial stability (e.g., distribution of saving rates) Useful for the public debate: Big demand for decompositions of growth by social groups Not possible today Next steps in the US DINA Adding all taxes and transfers—monetary and in kind—and imputations for public goods consumption Will make it possible to provide measures of: Consumption inequality Distribution of post-tax and transfer income How pre-tax and post-tax distributions differ → will give a comprehensive view of the redistributive effect of government
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