Lecture 1: Introduction to Graduate Public Economics

Stefanie Stantcheva

Fall 2016

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Our Goals for this class

1 Learn skills and methods (theory and empirical).2 Create a culture of key papers and read widely.3 Get you inspired and ready for your own research.

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Class Logistics

Meet once per week, 2.5-3 hours. Longer break halfway through.3 problem sets.One referee report.One final exam.I post office hours 1-2 weeks in advance – sign up whether class orresearch related.Reading group.What I expect from you.

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PUBLIC ECONOMICS DEFINITION

Acknowledgment: many slides are based on Emmanuel Saez’ grad econcourse at Berkeley.

Public economics = Study of the role of the government in the economy

Government is instrumental in most aspects of economic life:

1) Government in charge of huge regulatory structure

2) Taxes: governments in advanced economies collect 30-50% of NationalIncome in taxes

3) Expenditures: tax revenue funds traditional public goods (infrastructure,public order and safety, defense), and welfare state (education, retirementbenefits, health care, income support)

4) Macro-economic stabilization through central bank (interest rate,inflation control), fiscal stimulus, bailout policies

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20%

30%

40%

50%

60%

Tota

l tax

reve

nues

(% n

atio

nal i

ncom

e)

Figure 13.1. Tax revenues in rich countries, 1870-2010

Sweden

France

U.K.

U.S.,

0%

10%

1870 1890 1910 1930 1950 1970 1990 2010

Tota

l tax

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(% n

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Total tax revenues were less than 10% of national income in rich countries until 1900-1910; they represent between 30% and 55% of national income in 2000-2010. Sources and series: see piketty.pse.ens.fr/capital21c.

Source: Piketty (2014)

Two General Rules for Government Intervention

1) Failure of 1st Welfare Theorem: Government intervention can help ifthere are market or individual failures. Markets first, government second.Why?

2) Fallacy of the 2nd Welfare Theorem: Distortionary Governmentintervention is required to reduce economic inequality

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Role 1: 1st Welfare Theorem Failure

1st Welfare Theorem: If (1) no externalities, (2) perfect competition, (3)perfect information, (4) agents are rational, then private market equilibriumis Pareto efficient

Government intervention may be desirable if:

1) Externalities require government interventions (Pigouviantaxes/subsidies, public good provision)

2) Imperfect competition requires regulation (typically studied in IndustrialOrganization)

3) Imperfect or Asymmetric Information (e.g., adverse selection may call formandatory insurance)

4) Agents are not rational (= individual failures analyzed in behavioraleconomics, field in huge expansion): e.g., myopic or hyperbolic agents maynot save enough for retirement

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1. Externalities

Markets may be incomplete (e.g., smoking, pollution).

Achieving the Coasian efficient solution requires a coordinating institution,such as a government.

Public goods (infrastructure, defense, education).

Important question: what public goods to provide, how to correct forexternalities.

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2. Imperfect competition

Role for government regulation when markets are not competitive.

We will see some of this when we study R&D policies and innovation.

Typically we leave this to IO, but we shouldn’t!

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3. Imperfect and asymmetric information

Adverse Selection in health insurance (reason for mandated coverage).

Capital markets and credit constraints (subsidies for education).

Intergenerational issues (future generations may not be valuedappropriately in today’s market).

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4. Individual Failures

Behavioral issues, own-agency problems.

If agents do not optimize, may be best to intervene. E.g.: mandatedretirement savings.

Paternalism?

Currently very active area of research, theoretically and empirically.

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Individual Failures vs. Paternalism

In many situations, individuals may not or do not seem to act in their bestinterests [e.g., many individuals are not able to save for retirement]

Two Polar Views on such situations:

1) Individual Failures [Behavioral Economics View] Individual Failuresexist: Self-control problems, Cognitive Limitations

2) Paternalism [Libertarian Chicago View] Individual failures do not existand govt wants to impose on individuals its own preferences againstindividuals’ will

Key way to distinguish those 2 views: Under Paternalism, individualsshould be opposed to govt programs such as Social Security. If individualsunderstand they have failures, they will tend to support govt programs suchas Social Security.

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Role 2: 2nd Welfare Theorem Fallacy

Even with no market failures, free market might generate substantialinequality. Inequality is an issue because of people care about theirrelative situation.

2nd Welfare Theorem: Any Pareto Efficient outcome can be reached by (1)Suitable redistribution of initial endowments [individualized lump-sumtaxes based on indiv. characteristics and not behavior], (2) Then lettingmarkets work freely

⇒ No conflict between efficiency and equity [1st best taxation]

Redistribution of initial endowments is not feasible (information pb) ⇒ govtneeds to use distortionary taxes and transfers ⇒ Trade-off betweenefficiency and equity [2nd best taxation]

This class will focus on both roles, but first on 2).13 55

Illustration of 2nd Welfare Theorem Fallacy

Suppose economy is populated 50% with disabled people unable to work(hence they earn $0) and 50% with able people who can work and earn$100

Free market outcome: disabled have $0, able have $100

2nd welfare theorem: govt is able to tell apart the disabled from the able[even if the able do not work]

⇒ can tax the able by $50 [regardless of whether they work or not] to give $50 toeach disabled person ⇒ the able keep working [otherwise they’d have zeroincome and still have to pay $50]

Real world: govt can’t tell apart disabled from non working able

⇒ $50 tax on workers + $50 transfer on non workers destroys all incentives towork ⇒ govt can no longer do full redistribution ⇒ Trade-off between equity andsize of the pie

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Normative vs. Positive Public Economics

Normative Public Economics: Analysis of How Things Should be (e.g.,should the government intervene in health insurance market? how highshould taxes be?, etc.)

Positive Public Economics: Analysis of How Things Really Are (e.g., Doesgovt provided health care crowd out private health care insurance? Dohigher taxes reduce labor supply?)

Positive Public Economics is a required 1st step before we can completeNormative Public Economics

Positive analysis is primarily empirical and Normative analysis is primarilytheoretical

Positive Public Economics overlaps with Labor Economics

Political Economy is a positive analysis of govt outcomes [public choice ispolitical economy from a libertarian view]

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Plan for ec2450a Lectures (I)

(1) Optimal income taxation: Facts about inequality, theory.

(2) Optimal transfers: Optimal design, participation responses.

(3) Empirical evidence on responses to taxation: labor supply responses,migration, taxable income responses.

(4) Social preferences for redistribution: Theory and empirical studies.

(5) Tax avoidance, evasion, and enforcement

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Plan for ec2450a Lectures (II)

(6) Capital income taxation: canonical models, bequest and inheritancetaxation, a simpler theory, empirical evidence.

(7) Education and human capital

(8) New Dynamic Public Finance: methods and standard results,applications, life cycle taxation, recent topics.

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Plan for ec2450a Lectures (III)

(9) Policies for R&D and innovation: Theory of incentives and empiricalevidence.

(10) Corporate taxation: Theory and evidence on taxation, corporatefinancial policy, and business investment

(11) Social Security and Retirement: theory of social security, evidence onbehavioral responses to policies, tax-favored retirement accounts: IRAs,401(k)s.

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My research

I do mostly public, but mixed with labor, macro, and (some) politicaleconomy.

Both theory and empirical work.

Classical optimal tax theory (e.g.: labor market frictions, rent-seeking,capital taxation)

Dynamic new Public Finance and Mechanism Design (e.g.: humancapital, R&D incentives).

Social preference theory.

Empirical experimental work on preferences for redistribution.

Empirical effects of taxes (e.g: migration, innovation, capital income).

Innovation policy.19 55

Income Inequality: Labor vs. Capital Income

Individuals derive market income (before tax) from labor and capital:z = wl + rk where w is wage, l is labor supply, k is wealth, r is rate ofreturn on wealth

1) Labor income inequality is due to differences in working abilities(education, talent, physical ability, etc.), work effort (hours of work, effort onthe job, etc.), and luck (labor effort might succeed or not)

2) Capital income inequality is due to differences in wealth k (due to pastsaving behavior and inheritances received), and in rates of return r (variesdramatically overtime and across assets)

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Macro-aggregates: Labor vs. Capital Income

Labor income wl ' 75% of national income z

Capital income rk ' 25% of national income z (has increased in recentdecades)

Wealth stock k ' 400− 500% of national income z (is increasing)

Rate of return on capital r ' 5%

α = β · r where α = rk/z share of capital income and β = k/z wealth toincome ratio

In GDP, gross capital share is higher (35%) because it includes depreciationof capital (' 10% of GDP)

National Income = GDP - depreciation of capital + net foreign income21 55

0%

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Figure A6: The composition of capital income in the U.S., (details)

Housing rents (net of mortgages)

Noncorporate business profits

Net interest Corporate profits

Profits & interest paid to pensions

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UK France US South US North

% n

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Figure 11: National wealth in 1770-1810: Old vs. New world

Other domestic capital

Housing

Slaves

Agricultural Land

10%

15%

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25%

30%

35%

40%

1975 1980 1985 1990 1995 2000 2005 2010

Figure 12: Capital shares in factor-price national income 1975-2010

USA Japan Germany France UK Canada Australia Italy

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Source: Piketty and Zucman (2014)

200%

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500%

600%

700%

800%

Valu

e of

priv

ate

and

publ

ic c

apita

l (%

nat

iona

l inc

ome)

Figure 5.1. Private and public capital: Europe and America, 1870-2010

United States

Europe

Public

Private capital

-100%

0%

100%

200%

1870 1890 1910 1930 1950 1970 1990 2010

Valu

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The fluctuations of national capital in the long run correspond mostly to the fluctuations of private capital (both in Europe and in the U.S.). Sources and series: see piketty.pse.ens.fr/capital21c.

Public capital

Source: Piketty (2014)

Income Inequality: Labor vs. Capital Income

Capital Income (or wealth) is more concentrated than Labor Income. In theUS:

Top 1% wealth holders have 40% of total wealth (Saez-Zucman 2014).Bottom 50% wealth holders hold almost no wealth.

Top 1% incomes have 20% of total income (Piketty-Saez)

Top 1% labor income earners have about 15% of total labor income

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Income Inequality Measurement

Inequality can be measured by indexes such as Gini, log-variance, quantileincome shares which are functions of the income distribution F (z)

Gini = 2 * area between 45 degree line and Lorenz curve

Lorenz curve L(p) at percentile p is fraction of total income earned byindividuals below percentile p

0 ≤ L(p) ≤ p

Gini=0 means perfect equality

Gini=1 means complete inequality (top person has all the income)

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Key Empirical Facts on Income/Wealth Inequality Evolution

1) In the US, labor income inequality has increased substantially since1970: due to skilled biased technological progress vs. institutions (minwage and Unions) [Autor-Katz’99]

2) US top income shares dropped dramatically from 1929 to 1950 andincreased dramatically since 1980 [Piketty and Saez]

3) Top US incomes used to be primarily capital income. Now, top incomesare divided 50/50 between labor and capital income (explosion of top laborincomes with stock-options, etc.)

4) Fall in top income shares from 1900-1950 happened in most OECDcountries. Surge in top income shares has happened primarily in Englishspeaking countries, not as much in Continental Europe and Japan [Atkinson,Piketty, Saez JEL’11]

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1940 1950 1960 1970 1980 1990 20000.30

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Figure 1: Gini coefficient

Source: Kopczuk, Saez, Song QJE'10: Wage earnings inequality

25%

30%

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45%

50%

1917

19

22

1927

19

32

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1947

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52

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62

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72

1977

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82

1987

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92

1997

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02

2007

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Top

10%

Inco

me

Shar

e Top  10%  Pre-­‐tax  Income  Share  in  the  US,  1917-­‐2014  

   

Source: Piketty and Saez, 2003 updated to 2014. Series based on pre-tax cash market income including realized capital gains and excluding government transfers.

0%

5%

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25%

1913

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18

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Decomposing Top 10% into 3 Groups, 1913-2014

Top 1% (incomes above $423,000 in 2014)

Top 5-1% (incomes between $174,200 and $423,000)

Top 10-5% (incomes between $121,400 and $174,200)

Source: Piketty and Saez, 2003 updated to 2014. Series based on pre-tax cash market income including realized capital gains and excluding government transfers.

0%

2%

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93

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2008

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Top

0.1%

Inco

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Shar

e Top 0.1% US Pre-Tax Income Share, 1913-2014

Top 0.1% income share (incomes above $1.9m in 2014)

Source: Piketty and Saez, 2003 updated to 2014. Series based on pre-tax cash market income including or excluding realized capital gains, and always excluding government transfers.

0

10,000

20,000

30,000

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70,000

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Real average national income: Full adult population vs. bottom 90%

Real values are obtained by using the national income deflator and expressed in 2012 dollars. Source: Appendix Tables XX.

Bottom 90% adults

All adults

2.0%

2.0%

1.4%

0.7%

60%

65%

70%

75%

80%

85%

90%

1917

1922

1927

1932

1937

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2012

% o

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lth

Top 10% wealth share in the United States, 1917-2012

The figure depicts the share of total household wealth owned by the top 10%, obained by capitalizing income tax returns versus in the Survey of Consumer Finances. The unit of analysis is the familly. Source: Appendix Tables B1 and C4.

Capitalized income

SCF

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Top 0.1% wealth share in the United States, 1913-2012

This figure depicts the share of total household wealth held by the 0.1% richest families, as estimated by capitalizing income tax returns. In 2012, the top 0.1% includes about 160,000 families with net wealth above $20.6 million. Source: Appendix Table B1.

Key Empirical Facts on Income/Capital Inequality Cross-Sectionally

Based on IRS tax returns data from Saez and Zucman (2015) for 2007.

Fact 1: Capital income is more unequally distributed than laborincome.

Fact 2: At the top, total income is mostly capital income.

Fact 3: Two-dimensional heterogeneity: even conditional on laborincome, a lot of inequality in capital income.

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Labor, Capital, and Total Income Distributions (Fact 1)

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Labor, Capital, and Total Income Distributions (Fact 2)

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Capital Income Conditional on Labor Income (Fact 3)

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Measuring Intergenerational Income Mobility

Strong consensus that children’s success should not depend too much onparental income [Equality of Opportunity]

Studies linking adult children to their parents can measure link betweenchildren and parents income

Simple measure: average income rank of children by income rank ofparents [Chetty et al. 2014]

1) US has less mobility than European countries (especially Scandinaviancountries such as Denmark)

2) Substantial heterogeneity in mobility across cities in the US

3) Places with low race/income segregation, low income inequality, goodK-12 schools, high social capital, high family stability tend to have highmobility [these are correlations and do not imply causality]

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FIGURE II: Association between Children’s Percentile Rank and Parents’ Percentile Rank

A. Mean Child Income Rank vs. Parent Income Rank in the U.S.

2030

4050

6070

0 10 20 30 40 50 60 70 80 90 100

Mea

n C

hild

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Parent Income Rank

Rank-Rank Slope (U.S) = 0.341(0.0003)

B. United States vs. Denmark

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6070

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Mea

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Parent Income Rank United StatesDenmark

Rank-Rank Slope (Denmark) = 0.180(0.0063)

Notes: These figures present non-parametric binned scatter plots of the relationship between child and parent income ranks.Both figures are based on the core sample (1980-82 birth cohorts) and baseline family income definitions for parents andchildren. Child income is the mean of 2011-2012 family income (when the child was around 30), while parent income is meanfamily income from 1996-2000. We define a child’s rank as her family income percentile rank relative to other children inher birth cohort and his parents’ rank as their family income percentile rank relative to other parents of children in the coresample. Panel A plots the mean child percentile rank within each parental percentile rank bin. The series in triangles in PanelB plots the analogous series for Denmark, computed by Boserup, Kopczuk, and Kreiner (2013) using a similar sample andincome definitions (see text for details). The series in circles reproduces the rank-rank relationship in the U.S. from Panel Aas a reference. The slopes and best-fit lines are estimated using an OLS regression on the micro data for the U.S. and on thebinned series (as we do not have access to the micro data) for Denmark. Standard errors are reported in parentheses.

Source: Chetty, Hendren, Kline, Saez (2014)

FIGURE II: Association between Children’s Percentile Rank and Parents’ Percentile Rank

A. Mean Child Income Rank vs. Parent Income Rank in the U.S.

2030

4050

6070

0 10 20 30 40 50 60 70 80 90 100

Mea

n C

hild

Inco

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Parent Income Rank

Rank-Rank Slope (U.S) = 0.341(0.0003)

B. United States vs. Denmark

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4050

6070

0 10 20 30 40 50 60 70 80 90 100

Mea

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Parent Income Rank United StatesDenmark

Rank-Rank Slope (Denmark) = 0.180(0.0063)

Notes: These figures present non-parametric binned scatter plots of the relationship between child and parent income ranks.Both figures are based on the core sample (1980-82 birth cohorts) and baseline family income definitions for parents andchildren. Child income is the mean of 2011-2012 family income (when the child was around 30), while parent income is meanfamily income from 1996-2000. We define a child’s rank as her family income percentile rank relative to other children inher birth cohort and his parents’ rank as their family income percentile rank relative to other parents of children in the coresample. Panel A plots the mean child percentile rank within each parental percentile rank bin. The series in triangles in PanelB plots the analogous series for Denmark, computed by Boserup, Kopczuk, and Kreiner (2013) using a similar sample andincome definitions (see text for details). The series in circles reproduces the rank-rank relationship in the U.S. from Panel Aas a reference. The slopes and best-fit lines are estimated using an OLS regression on the micro data for the U.S. and on thebinned series (as we do not have access to the micro data) for Denmark. Standard errors are reported in parentheses.

Source: Chetty, Hendren, Kline, Saez (2014)

§  Probability that a child born to parents in the bottom fifth of the income distribution reaches the top fifth:

à Chances of achieving the “American Dream” are almost two times higher in Canada than in the U.S.

Canada

Denmark

UK

USA

13.5%

11.7%

7.5%

9.0% Blanden and Machin 2008

Boserup, Kopczuk, and Kreiner 2013

Corak and Heisz 1999

Chetty, Hendren, Kline, Saez 2014

The American Dream? Source: Chetty et al. (2014)

Note: Lighter Color = More Upward Mobility Download Statistics for Your Area at www.equality-of-opportunity.org

The Geography of Upward Mobility in the United States Probability of Reaching the Top Fifth Starting from the Bottom Fifth

US average 7.5% [kids born 1980-2]

Source: Chetty et al. (2014)

The Geography of Upward Mobility in the United States Odds of Reaching the Top Fifth Starting from the Bottom Fifth

SJ 12.9%

LA 9.6%

Atlanta 4.5%

Washington DC 11.0%

Charlotte 4.4%

Indianapolis 4.9%

Note: Lighter Color = More Upward Mobility Download Statistics for Your Area at www.equality-of-opportunity.org

SF 12.2%

San Diego 10.4%

SB 11.3%

Modesto 9.4% Sacramento 9.7%

Santa Rosa 10.0%

Fresno 7.5%

US average 7.5% [kids born 1980-2]

Bakersfield 12.2%

Source: Chetty et al. (2014)

Pathways • The Poverty and Inequality Report 2015

40 economic mobility

that much of the variation in upward mobility across areas may be driven by a causal effect of the local environment rather than differences in the characteristics of the people who live in different cities. Place matters in enabling intergen-erational mobility. Hence it may be effective to tackle social mobility at the community level. If we can make every city in America have mobility rates like San Jose or Salt Lake City, the United States would become one of the most upwardly mobile countries in the world.

Correlates of spatial VariationWhat drives the variation in social mobility across areas? To answer this question, we begin by noting that the spatial pattern in gradients of college attendance and teenage birth rates with respect to parent income is very similar to the spa-tial pattern in intergenerational income mobility. The fact that much of the spatial variation in children’s outcomes emerges before they enter the labor market suggests that the differ-ences in mobility are driven by factors that affect children while they are growing up.

We explore such factors by correlating the spatial variation in mobility with observable characteristics. We begin by show-ing that upward income mobility is significantly lower in areas with larger African-American populations. However, white individuals in areas with large African-American populations also have lower rates of upward mobility, implying that racial shares matter at the community (rather than individual) level. One mechanism for such a community-level effect of race is segregation. Areas with larger black populations tend to be more segregated by income and race, which could affect both

white and black low-income individuals adversely. Indeed, we find a strong negative correlation between standard mea-sures of racial and income segregation and upward mobility. Moreover, we also find that upward mobility is higher in cities with less sprawl, as measured by commute times to work. These findings lead us to identify segregation as the first of five major factors that are strongly correlated with mobility.

The second factor we explore is income inequality. CZs with larger Gini coefficients have less upward mobility, consistent with the “Great Gatsby curve” documented across countries.7 In contrast, top 1 percent income shares are not highly cor-related with intergenerational mobility both across CZs within the United States and across countries. Although one can-not draw definitive conclusions from such correlations, they suggest that the factors that erode the middle class hamper intergenerational mobility more than the factors that lead to income growth in the upper tail.

Third, proxies for the quality of the K–12 school system are also correlated with mobility. Areas with higher test scores (controlling for income levels), lower dropout rates, and smaller class sizes have higher rates of upward mobility. In addition, areas with higher local tax rates, which are predomi-nantly used to finance public schools, have higher rates of mobility.

Fourth, social capital indices8—which are proxies for the strength of social networks and community involvement in an area—are very strongly correlated with mobility. For instance, areas of high upward mobility tend to have higher fractions

Rank Commuting Zone odds of Reaching Top fifth from Bottom fifth

Rank Commuting Zone odds of Reaching Top fifth from Bottom fifth

1 San Jose, CA 12.9% 41 Cleveland, OH 5.1%

2 San Francisco, CA 12.2% 42 St. Louis, MO 5.1%

3 Washington, D.C. 11.0% 43 Raleigh, NC 5.0%

4 Seattle, WA 10.9% 44 Jacksonville, FL 4.9%

5 Salt Lake City, UT 10.8% 45 Columbus, OH 4.9%

6 New York, NY 10.5% 46 Indianapolis, IN 4.9%

7 Boston, MA 10.5% 47 Dayton, OH 4.9%

8 San Diego, CA 10.4% 48 Atlanta, GA 4.5%

9 Newark, NJ 10.2% 49 Milwaukee, WI 4.5%

10 Manchester, NH 10.0% 50 Charlotte, NC 4.4%

Table 1. upward Mobility in the 50 largest Metro areas: The Top 10 and bottom 10

Note: This table reports selected statistics from a sample of the 50 largest commuting zones (CZs) according to their populations in the 2000 Census. The columns report the percentage of children whose family income is in the top quintile of the national distribution of child family income conditional on having parent family income in the bottom quintile of the parental national income distribution—these probabilities are taken from Online Data Table VI of Chetty et al., 2014a.

Source: Chetty et al., 2014a.

Source: Chetty et al. (2014)

Govt Redistribution with Taxes and Transfers

Govt taxes individuals based on income and consumption and providestransfers: z is pre-tax income, y = z −T (z) + B(z) is post-tax income

1) If inequality in y is less than inequality in z ⇔ tax and transfer systemis redistributive (or progressive)

2) If inequality in y is more than inequality in z ⇔ tax and transfer systemis regressive

a) If y = z · (1− t) with constant t , tax/transfer system is neutral

b) If y = z · (1− t) + G where G is a universal (lumpsum) allowance, thentax/transfer system is progressive

c) If y = z −T where T is a uniform tax (poll tax), then tax/transfer system isregressive

Current tax/transfer systems in rich countries look roughly like b)46 55

Federal US Tax System: Overview

1) Individual income tax (on both labor+capital income) [progressive](40% offed tax revenue)

2) Payroll taxes (on labor income) financing social security programs [aboutneutral] (40% of revenue)

3) Corporate income tax (on capital income) [progressive if incidence oncapital income] (15% of revenue)

4) Estate taxes (on capital income) [very progressive] (2% of revenue)

5) Minor excise taxes [regressive] (3% of revenue)

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State+Local Tax System: Overview

1) Individual+Corporate income taxes [progressive] (30% of state+local taxrevenue)

2) Sales + Excise taxes (tax on consumption = income - savings) [aboutneutral] (30% of revenue)

3) Real estate property taxes (on capital income) [slightly progressive] (30%of revenue)

http://www.census.gov/govs/www/qtax.html

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Public Finance and Public Policy Jonathan Gruber Third Edition Copyright © 2010 Worth Publishers

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Distribution of Revenue Sources

1.2

The Distribution of Federal and State Revenues, 1960 and 2008 • This figure shows the changing composition of federal and state revenue sources over time, as a share of total revenues. (a) At the federal level, there has been a large reduction in corporate and excise tax revenues and a rise in payroll tax revenues. (b) For the states, there has been a decline in property taxes and a rise in income taxes and federal grants.

US Tax System: Progressivity and Evolution

1) Medium Term Changes: Federal Tax Progressivity has declined since1970 but govt redistribution remains substantial especially when includingtransfers (Medicaid, Social Security retirement, DI, UI various incomesupport programs)

2) Long Term Changes: Before 1913, US taxes were primarily tariffs,excises, and real estate property taxes [slightly regressive], minimal welfarestate (and hence small govt)

http://www.treasury.gov/education/fact-sheets/taxes/ustax.shtml

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2. Federal Average Tax Rates by Income Groups (individual+corporate+payroll+estate taxes)

0%

10%

20%

30%

40%

50%

60%

70%

80%P0

-20

P20-

40

P40-

60

P60-

80

P80-

90

P90-

95

P95-

99

P99-

99.5

P99.

5-99

.9

P99.

9-99

.99

P99.

99-1

00

1970

2000

2005

Source: Piketty and Saez JEP'07

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Distribution of Spending

1.2

The Distribution of Federal and State Expenditures, 1960 and 2007 • This figure shows the changing composition of federal and state spending over time, as a share of total spending. (a) For the federal government, defense spending has fallen and Social Security and health spending have risen. (b) For the states, the distribution has been more constant, with a small decline in education and welfare spending and a rise in health spending.

REFERENCES CITED

Atkinson, A., F. Alvaredo, T. Piketty, E. Saez, and G. Zucman World Wealth andIncome Database, (web)

Atkinson, A., T. Piketty and E. Saez “Top Incomes in the Long Run of History”,Journal of Economic Literature 49(1), 2011, 30–71. (web)

Chetty, Raj, Nathan Hendren, Patrick Kline, and Emmanuel Saez, “Where is theLand of Opportunity? The Geography of Intergenerational Mobility in the UnitedStates,” Quarterly Journal of Economics, 129(4), 2014, 1553-1623. (web)

Kopczuk, Wojciech, Emmanuel Saez, and Jae Song “Earnings Inequality andMobility in the United States: Evidence from Social Security Data since 1937,”Quarterly Journal of Economics 125(1), 2010, 91-128. (web)

Piketty, Thomas, Capital in the 21st Century, Cambridge: Harvard UniversityPress, 2014, (web)

Piketty, Thomas and Emmanuel Saez “Income Inequality in the United States,1913-1998”, Quarterly Journal of Economics, 118(1), 2003, 1-39. (web)

Piketty, Thomas and Emmanuel Saez “How Progressive is the U.S. Federal TaxSystem? A Historical and International Perspective,” Journal of EconomicPerspectives, 21(1), Winter 2007, 3-24. (web)

Piketty, Thomas, Emmanuel Saez, and Gabriel Zucman, “Distributional NationalAccounts: Methods and Estimates for the United States, 1913-2014”, workingpaper in preparation, 2016

Piketty, Thomas and Gabriel Zucman, “Capital is Back: Wealth-Income Ratios inRich Countries, 1700-2010”, Quarterly Journal of Economics 129(3), 2014,1255-1310 (web)

Saez, Emmanuel and Gabriel Zucman, “Wealth Inequality in the United Statessince 1913: Evidence from Capitalized Income Tax Data”, forthcoming QuarterlyJournal of Economics (web)

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GENERAL BOOK REFERENCES

Graduate Level

Atkinson, A.B. and J. Stiglitz, Lectures on Public Economics, New York: McGrawHill, 1980.

Auerbach, A. and M. Feldstein, eds., Handbook of Public Economics, 4 Volumes,Amsterdam: North Holland, 1985, 1987, 2002, and 2002. (web)

Auerbach, A., Chetty, R., M. Feldstein, and E. Saez, eds., Handbook of PublicEconomics, Volume 5, Amsterdam: North Holland, 2013 (web)

Kaplow, L. The Theory of Taxation and Public Economics. Princeton UniversityPress, 2008.

Mirrlees, J. Reforming the Tax System for the 21st Century The Mirrlees Review,Oxford University Press, (2 volumes) 2009 and 2010. (web)

Piketty, Thomas, Capital in the 21st Century, Cambridge: Harvard UniversityPress, 2014, (web)

Salanié, B. The Economics of Taxation, Cambridge: MIT Press, 2nd Edition 2010.

Slemrod, Joel and Christian Gillitzer. Tax Systems, Cambridge: MIT Press, 2014.

Under-Graduate Level

Gruber, J. Public Finance and Public Policies, 5th edition, Worth Publishers, 2015.

Rosen, H. and T. Gayer Public Finance, 10th edition, McGraw Hill, 2013.

Stiglitz, J. and J. Rosengard. Economics of the Public Sector, 4th edition, Norton,2015.

Slemrod, J. and J. Bakija. Taxing Ourselves: A Citizen’s Guide to the Debate overTaxes. 4th edition, MIT Press, 2008.

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REFERENCES ON EMPIRICAL METHODS:

Angrist, J. and A. Krueger, “Instrumental Variables and the Search forIdentification: From Supply and Demand to Natural Experiments,” Journal ofEconomic Perspectives, 15 (4), 2001, 69-87 (web)

Angrist, J. and Steve Pischke. Mostly Harmless Econometrics: An Empiricist’sCompanion, Princeton University Press, 2009.

Bertrand, M. E. Duflo et S. Mullainhatan, “How Much Should we TrustDifferences-in-Differences Estimates?,” Quarterly Journal of Economics, Vol. 119,No. 1, 2004, pp. 249-275. (web)

Imbens, Guido and Jeffrey Wooldridge (2007) What’s New in Econometrics? NBERSUMMER INSTITUTE MINI COURSE 2007. (web)

Meyer, B. “Natural and Quasi-Experiments in Economics,” Journal of Business andEconomic Statistics, 13(2), April 1995, 151-161. (web)

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