Using Federal Administrative Data to 

Evaluate and Improve Economic Policy

Raj Chetty

Stanford and Harvard

The opinions expressed in this paper are those of the authors alone and do not necessarily reflect the views of

the Internal Revenue Service or the U.S. Treasury Department. This work is a component of a larger project

examining the effects of eliminating tax expenditures on the budget deficit and economic activity, approved

under IRS contract TIRNO-12-P-00374.
Use of Administrative Data in Publications in Economics Journals, 1980-2010

Empirical Studies Based on Micro Data in Developed Countries

Micro-data Based Articles using Admin. Data (%)

1980 1990 2000 2010 Year

100

80

60

40

20

0

AER JPE QJE ECMA

Note: “Administrative” datasets refer to any dataset that was collected without directly surveying 

individuals (e.g., scanner data, stock prices, school district records, social security records). 

Sample excludes studies whose primary data source is from developing countries.
Why the Shift Toward Administrative Data?

 Administrative data has great value for several reasons:

1. Comprehensive, high quality data  gold-standard descriptive 

statistics

2. Large samples  quasi-experimental methods of causal 

inference

3. Longitudinal tracking without attrition  long-term evaluations

4. Ability to link other datasets  rich set of outcomes
Administrative Data in the United States

 Researchers are shifting away from studying the U.S. 

because admin. data are more accessible in Europe 

– U.S. government agencies have made important strides to 

counter this trend in recent years, but more work is needed

 Goal of this talk: illustrate the benefits of administrative 

data and downstream impacts on policy in the U.S.

– Discuss recent studies in our research group that analyze how to 

improve equality of opportunity in America

– Part of a larger project studying tax expenditures in the U.S.
The American Dream?

 Probability that a child born to parents in the bottom fifth 

of the income distribution reaches the top fifth:
The American Dream?

 Probability that a child born to parents in the bottom fifth 

of the income distribution reaches the top fifth:

Canada

Denmark

UK

USA

13.5%

11.7%

7.5%

Blanden and Machin 2008 9.0%

Boserup, Kopczuk, and Kreiner 2013

Corak and Heisz 1999

Chetty, Hendren, Kline, Saez 2014
The American Dream?

 Probability that a child born to parents in the bottom fifth 

of the income distribution reaches the top fifth:

Canada

Denmark

UK

USA

13.5%

11.7%

7.5%

Blanden and Machin 2008 9.0%

Boserup, Kopczuk, and Kreiner 2013

Corak and Heisz 1999

Chetty, Hendren, Kline, Saez 2014

 Chances of achieving the “American Dream” are almost 

two times higher in Canada than in the U.S.
Differences in Opportunity Within the U.S.

 Research on mobility has traditionally focused on 

differences across countries

 But social mobility varies even more within the U.S.

 We calculate upward mobility for every metro and rural 

area in the U.S.

– Use anonymous data on earnings draw from tax records on 40 

million children born between 1980-1993

Source: Chetty, Hendren, Kline, Saez QJE 2014: The Equality of Opportunity Project
The Geography of Upward Mobility in the United States

Chances of Reaching the Top Fifth Starting from the Bottom Fifth by Metro Area

San

Jose 

12.9%

Salt Lake City 10.8% Atlanta 4.5%

Washington DC 11.0%

Charlotte 4.4%

Denver 8.7%

Note: Lighter Color = More Upward Mobility

Download Statistics for Your Area at www.equality-of-opportunity.org

Boston 10.4%

Minneapolis 8.5%

Chicago

6.5%
The Geography of Upward Mobility in the Washington Metro Area

Odds of Reaching the Top Fifth Starting from the Bottom Fifth by County

Charles

Baltimore

DC

Hartford

Baltimore: 3.5%

District of Columbia: 4.7%

Montgomery: 16.1%

Prince 

George’s
Causal Effects of Place vs. Sorting

 Two very different explanations for variation in children’s 

outcomes across areas:

1. Heterogeneity: different people live in different places

2. Neighborhood effects: places have a causal effect on 

upward mobility for a given person
Causal Effects of Place vs. Sorting

 Ideal experiment: randomly assign children to 

neighborhoods and compare outcomes in adulthood

 We approximate this experiment using a quasiexperimental design [Chetty and Hendren 2015]

– Study families who move across areas with children of different 

ages in observational data
0% 20% 40% 60% 80% 100%

10 15 20 25 30

Age of Child when Parents Move

Percentage Gain from Moving to a Better Area

Effects of Moving to a Different Neighborhood 

on a Child’s Income in Adulthood by Age at Move

Boston

Chicago
0% 20% 40% 60% 80% 100%

10 15 20 25 30

Age of Child when Parents Move

Effects of Moving to a Different Neighborhood 

on a Child’s Income in Adulthood by Age at Move

Percentage Gain from Moving to a Better Area

Boston

Chicago

Suppose the mean earnings of children who grew up from

birth in Boston is $1,000 higher than the mean earnings of

children who grew up in Chicago. Moving at age 9 from

area Chicago to Boston is associated with a earnings gain

of $540 (54%) on average.
0% 20% 40% 60% 80% 100%

10 15 20 25 30

Age of Child when Parents Move

Effects of Moving to a Different Neighborhood 

on a Child’s Income in Adulthood by Age at Move

Percentage Gain from Moving to a Better Area

Boston

Chicago
Identifying Causal Exposure Effects

 Key assumption underlying quasi-experimental design: 

selection effect does not vary with child’s age at move

 This assumption could be violated through two channels:

1. Parents who move to good areas when their children are young 

may invest more in their children in other ways

2. Moving may be correlated with other factors (e.g. change in 

parent income) that affect children directly
Identifying Causal Exposure Effects

 Address these concerns using two approaches:

1. Sibling comparisons: replicate baseline analysis with family 

fixed effects

– When a family movers to a better area, we find that younger sibling 

does better than older sibling on avg., in proportion to age gap 

2. Placebo tests exploiting heterogeneity across subgroups

– Some areas produce better outcomes for boys than girls

– When a family moves to an area that produces better outcomes for 

boys but not girls, son’s outcomes improve but daughter’s do not
Two Policy Approaches to Improving Upward Mobility

 Importance of place for mobility suggests two types of 

policy approaches:

1. Help people move to better areas

2. Invest in places with low levels of opportunity to 

replicate successes of areas with high upward mobility
Policy Approach 1: Moving to Opportunity

 One way to help low-income families move to better 

neighborhoods: housing vouchers

 HUD Moving to Opportunity Experiment: gave such 

vouchers using a randomized lottery [Ludwig et al. 2013]

 4,600 families in Boston, New York, LA, Chicago, and Baltimore in 

mid 1990’s
Control

King Towers

Harlem

Experimental

Wakefield 

Bronx

Most Common MTO Residential Locations in New York
MTO Experiment: Exposure Effects?

 Prior research on MTO (including HUD’s Final Impacts 

Evaluation) found no economic gains from moving

– But that work focused on adults and older youth at point of move

 We analyze long-term impacts of MTO on children who 

moved when young by linking MTO data to tax data
Moving to Opportunity Experiment

 Children who moved to low-poverty areas when young 

(e.g., below age 13) do much better as adults:

– 30% higher earnings = $100,000 gain over life in present value

– 27% more likely to attend college

– 30% less likely to become single parents

 But moving had little effect on the outcomes of children 

who were already teenagers

 Moving also had no effect on parents’ earnings

 Reinforces conclusion that childhood exposure is a key 

determinant of upward mobility
Policy Approach 2: Improving Neighborhoods

 Limits to scalability of policies that move people

 Also need policies that improve existing neighborhoods

 Challenging to identify causal effects of local policies

 But we can characterize the features of areas that generate 

good outcomes
What are the Characteristics of High-Mobility Areas?

Five Strongest Correlates of Upward Mobility

1. Segregation

– Racial and income segregation associated with less mobility

– Long commute times (sprawl) associated with less mobility
What are the Characteristics of High-Mobility Areas?

Five Strongest Correlates of Upward Mobility

1. Segregation

2. Income Inequality

– Places with smaller middle class have much less mobility
What are the Characteristics of High-Mobility Areas?

Five Strongest Correlates of Upward Mobility

1. Segregation

2. Income Inequality

3. Family Structure

– Areas with more single parents have much lower mobility

– Strong correlation even for kids whose own parents are married
What are the Characteristics of High-Mobility Areas?

Five Strongest Correlates of Upward Mobility

1. Segregation

2. Income Inequality

3. Family Structure

4. Social Capital

– “It takes a village to raise a child”

– Putnam (1995): “Bowling Alone”
What are the Characteristics of High-Mobility Areas?

Five Strongest Correlates of Upward Mobility

1. Segregation

2. Income Inequality

3. Family Structure

4. Social Capital

5. School Quality

– Greater expenditure, smaller classes, higher test scores 

correlated with more mobility

– Clear evidence of causal effects here
Using Administrative Data to Study Teachers’ Impacts

School district records

2.5 million children

18 million test scores

Tax records

Earnings, College 

Attendance, Teen Birth

Source: Chetty, Friedman, Rockoff 2014a,b
Measuring Teacher Quality: Test-Score Based Metrics

One prominent measure 

of teacher quality: 

teacher value-added

How much does a 

teacher raise her/his 

students’ test scores 

on average?
50

52

54

56

‘93 ‘94 ‘95 ‘96 ‘97 ‘98

Scores in 4th Grade Scores in 3rd Grade

School Year

Average Test Score

Entry of Teacher

with VA in top 5%

A Quasi-Experiment: Entry of High Value-Added Teacher
51

52

53

54

55

‘93 ‘94 ‘95 ‘96 ‘97 ‘98

Scores in 4th Grade Scores in 3rd Grade

School Year

Average Test Score

Entry of Teacher

with VA in bottom 5%

A Quasi-Experiment: Entry of Low Value-Added Teacher

50
Teacher Quality (Value-Added) Percentile

5th 95th Median

The Value of Improving Teacher Quality
+$50,000 lifetime earnings per child

= $1.4 million per classroom of 28 students

= $250,000 in present value at 5% int. rate

Teacher Quality (Value-Added) Percentile

5th 95th Median

The Value of Improving Teacher Quality
Equality of Opportunity and Economic Growth

 Traditional argument for greater social mobility is based 

on principles of justice

 But improving opportunities for upward mobility can also 

increase size of the economic pie

– One child’s success need not come at another’s expense

 To illustrate, focus on innovation

– Study the lives of 750,000 patent holders in the U.S. by linking 

universe of patent data to tax records

Source: Bell, Chetty, Jaravel, Petkova, van Reenen 2015
Patent rate for children

with parents in top 1%:

22.5 per 10,000

Patent Rates vs. Parent Income Percentile

0

5 10 15 20 25

0 20 40 60 80 100

Parent Income Percentile

Inventors per Ten Thousand

Patent rate for children 

with parents below median:

2.2 per 10,000
-2 -1 0 1 2

Patent Rates vs. 3rd Grade Test Scores

Inventors per Ten Thousand 15

20

5

0

10

3rd Grade Math Test Score (Standard Deviations Relative to Mean)

85th Percentile
Parent Income Below Median Parent Income Above Median

Patent Rates vs. 3rd Grade Test Scores

for Children with Low vs. High Income Parents

0

5 10 15 20

-2 -1 0 1 2

Inventors per Ten Thousand

3rd Grade Math Test Score (Standard Deviations Relative to Mean)
Parent Income Below Median Parent Income Above Median

Patent Rates vs. 3rd Grade Test Scores

for Children with Low vs. High Income Parents

0

5 10 15 20

-2 -1 0 1 2

Inventors per Ten Thousand

High-ability children much more 

likely to become inventors if they 

are from high-income families

3rd Grade Math Test Score (Standard Deviations Relative to Mean)
Policy Lessons

1. Improve childhood environments and primary education

 Not just spending more money: US already spends more than 

other developed countries with better outcomes

 Instead, focus on key inputs such as attracting and retaining

talented teachers (e.g., Finland)
Policy Impacts

“We know a good teacher can increase the 

lifetime income of a classroom by over 

$250,000.... Every person in this chamber 

can point to a teacher who changed the 

trajectory of their lives”

- Barack Obama, State of the Union, 2012

“A recent study by Harvard and Columbia 

economists found that students with 

effective teachers are less likely to become 

pregnant, more likely to go to college and 

more likely to get higher-paying 

jobs....Ineffective teachers are hurting our 

students’ futures – we can’t allow that.”

- Michael Bloomberg, State of the City, 2012
Policy Impacts
Policy Lessons

1. Improve childhood environments and primary education

2. Tackle social mobility at a local, not national level

 Focus on specific cities such as Baltimore and Chicago, and 

on specific neighborhoods within those cities

 Help families with young children move to high opportunity

areas using housing vouchers or tax credits 

 Working with HUD to develop ways to make Section 8 

voucher program more effective in achieving this goal

 Invest in improving neighborhoods with low mobility
Policy Lessons

1. Improve childhood environments and primary education

2. Tackle social mobility at a local, not national level

3. Harness administrative data to develop a scientific 

evidence base for economic and social policy

 Identify which neighborhoods are in greatest need of 

improvement and which policies work

 County-level data on mobility publicly available at 

www.equality-of-opportunity.org
Download County-Level Data on Social Mobility in the U.S.

www.equality-of-opportunity.org/data