Centre for Economic Performance Lionel Robbins Memorial Lectures … · 2017-07-11 · Centre for...

Centre for Economic Performance Lionel Robbins Memorial

Lectures

Policies to Improve Upward Mobility

Professor Raj Chetty

Professor of Economics, Stanford University

Hashtag for Twitter users: #LSERobbins

Professor Robin Burgess

Chair, LSE

Raj Chetty

Stanford University

Improving Equality of Opportunity New Lessons from Big Data

Lecture 2: Policies to Improve Upward Mobility

Photo Credit: Florida Atlantic University

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

How can we improve social mobility in areas with low rates of mobility?

Focus on two of the correlations identified in the last lecture

1. Segregation

2. School Quality

3. Income Inequality

4. Family Structure

5. Social Capital

Improving Social Mobility: Two Policy Questions

Lecture 2 Outline

1. Segregation and Housing Policy

2. Education Policy

Lecture 2 is based primarily on two sets of papers:

1. Chetty, Hendren, Katz. “The Long-Term Effects of Exposure to Better

Neighborhoods: New Evidence from the Moving to Opportunity Experiment”

AER 2016.

2. Chetty, Friedman, Rockoff. “Measuring the Impacts of Teachers I and II”

AER 2014a,b

Part 1

Segregation and Housing Policy

Many potential policies to try to promote integration:

– Subsidized housing vouchers to rent better apartments

– Mixed-income affordable housing developments

– Changes in zoning regulations and building restictions

Are such housing policies effective in increasing social mobility?

Standard economic theory actually predicts that cash grants of an

equivalent dollar amount are better

Segregation and Affordable Housing Policies

Understanding effects of housing policies on mobility is of particular

interest given the amount governments spend on such policies

U.S. spends $45 billion per year on housing vouchers, tax credits for

developers, and public housing

Are these policies effective, and how can they be better designed if

one wishes to improve social mobility?

Segregation and Affordable Housing Policies

Begin with an empirical analysis of the effectiveness of housing

voucher subsidies

Moving to Opportunity Experiment implemented from 1994-1998

4,600 families at 5 sites: Baltimore, Boston, Chicago, LA, New York

Families randomly assigned to one of three groups:

1. Experimental: housing vouchers restricted to low-poverty (<10%)

Census tracts

2. Section 8: conventional housing vouchers, no restrictions

3. Control: public housing in high-poverty (50% at baseline) areas

Moving to Opportunity Experiment

Control

ML King

Towers

Harlem

Experimental

Wakefield

Bronx

Common MTO Residential Locations in New York

Section 8

Soundview

Bronx

MTO Experiment: Exposure Effects?

Prior research on MTO has found little impact of moving to a better

area on economic outcomes such as earnings

– But has focused on adults and older youth at point of move [e.g., Kling, Liebman, and Katz 2007]

We test for exposure effects among children

– Does MTO improve outcomes for children who moved when

young?

– Link MTO to tax data to study children’s outcomes in mid 20’s

(a) Earnings (b) College Attendance

Impacts of MTO on Children Below Age 13 at Random Assignment

50

00

7

00

0

90

00

11

00

0

13

00

0

15

00

0

17

00

0

0

5

10

1

5

20

2

5

$11,270 $12,994 $14,747 16.5% 18.0% 21.7%

p = 0.014 p = 0.101 p = 0.435 p = 0.028

Control Section 8

Control Section 8

Experimental

Voucher

Experimental

Voucher

Indiv

idual E

arn

ings a

t A

ge ≥

24 (

$)

Colle

ge A

tten

dance, A

ges 1

8-2

0 (

%)

(c) Neighborhood Quality (d) Fraction Single Mothers

Impacts of MTO on Children Below Age 13 at Random Assignment

15

1

7

19

2

1

23

2

5

0

12

.5

25

3

7.5

23.8% 21.7% 20.4% 33.0% 31.0% 23.0%

p = 0.007 p = 0.046 p = 0.610 p = 0.042

Control Section 8

Control Section 8

Experimental

Voucher

Experimental

Voucher

Zip

Povert

y S

hare

(%

)

Bir

th w

ith n

o F

ath

er

Pre

sent

(%)

Impacts of MTO on Children Age 13-18 at Random Assignment

(a) Earnings

50

00

7

00

0

90

00

11

00

0

13

00

0

15

00

0

17

00

0

$15,882 $13,830 $13,455

p = 0.259 p = 0.219

Control Section 8

Experimental

Voucher

Indiv

idual E

arn

ings a

t A

ge ≥

24 (

$)

(b) Fraction Single Mothers

0

12

.5

25

3

7.5

5

0

62

.5

41.4% 40.2% 51.8%

p = 0.857 p = 0.238

Control Section 8

Experimental

Voucher B

irth

with n

o F

ath

er

Pre

sent

(%

)

Impacts of Experimental Voucher by Age of Random Assignment

Household Income, Age ≥ 24 ($)

-60

00

-4

00

0

-20

00

0

2

00

0

40

00

Exp

erim

en

tal V

s.

Co

ntr

ol IT

T o

n I

nco

me

($

)

10 12 14 16 Age at Random Assignment

Impacts of Moving to Opportunity on Adults’ Earnings

50

00

7

00

0

90

00

11

00

0

13

00

0

15

00

0

17

00

0

Indiv

idual E

arn

ings a

t A

ge ≥

24 (

$)

Control Section 8 Experimental

Voucher

$14,381 $14,778 $13,647

p = 0.569 p = 0.711

Housing vouchers can be very effective but must be targeted carefully

1. Vouchers should be targeted at families with young children

– Current U.S. policy of putting families on waitlists is especially

inefficient

Implications for Housing Voucher Policy

Housing vouchers can be very effective but must be targeted carefully

1. Vouchers should be targeted at families with young children

2. Vouchers should explicitly encourage families to move to affordable,

high-opportunity areas (opportunity bargains)

– In MTO experiment, unrestricted “Section 8” vouchers produced

smaller gains even though families could have made same moves

– Low-income families rarely use cash transfers to move to better

neighborhoods [Jacob et al. QJE 2015]

– These results suggest that behavioral biases may be important in

understanding housing choices [Chetty AER 2015]

Implications for Housing Voucher Policy

1. Costs: areas that offer better opportunity tend to be more expensive

• There are many opportunity bargains: areas with relatively low

rents that offer good opportunities for kids

Integration through Housing Vouchers: Potential Concerns

Soundview

Wakefield

Park Slope

East Midwood

Rockaway Park

MLK Towers

(Harlem)

-30

-2

0

-10

0

1

0

20

Oppo

rtu

nity I

nde

x:

Effect o

n C

hild

’s E

arn

ing

s (

%)

500 1,000 1,500 2,000

Average Monthly Rent in ZIP Code ($)

Opportunity Bargains at the ZIP Code Level in the New York Area


• There are many opportunity bargains: areas with relatively low

rents that offer good opportunities for kids

• Vouchers can save taxpayers money relative to public housing

projects in long run


Impacts of MTO on Annual Income Tax Revenue in Adulthood

for Children Below Age 13 at Random Assignment

0

20

0

40

0

60

0

80

0

10

00

1

20

0

Annu

al In

com

e T

ax R

evenue, A

ge ≥

24 (

$)

$447.5 $616.6 $841.1

p = 0.061 p = 0.004

Control Section 8 Experimental

Voucher


2. Negative spillovers: does integration hurt the rich?

• Empirically, more integrated neighborhoods do not have worse

outcomes for the rich


Salt Lake City Charlotte

20

3

0

40

5

0

60

7

0

0 20 40 60 80 100

Parent Percentile in National Income Distribution

Ch

ild P

erc

entile

in N

ation

al In

com

e D

istr

ibu

tion

Intergenerational Mobility in Salt Lake City vs. Charlotte


2. Negative spillovers: does integration hurt the rich?

3. Limits to scalability: general equilibrium effects

• Moving everyone in Harlem to Bronx is unlikely to have significant

effects

• Ultimately need to turn to policies that increase integration in other

ways rather than moving low-income families


A variety of other policies could potentially increase residential housing

integration

– Providing tax credits to encourage building affordable properties in

higher-income neighborhoods

– Retaining housing options for low and middle income families as

city centers gentrify

– Improved urban planning, e.g. changes in zoning regulations and

transportation

– Investing in local public infrastructure, such as schools

Place-Based Approaches to Integration

Part 2

Education Policies

Improving school quality is one widely discussed approach to

improving children’s outcomes

But simply spending more on schools may not be enough

[Hanushek 2001]

– U.S. spends more than most other developed countries on

education, yet has significantly worse outcomes

Need to understand which educational inputs matter most for

children’s long-term success: class size, teachers, resources?

– Each of these has been studied in detail in recent work; focus

here on teachers as an example

Education Policy

Tax records

Earnings, College Attendance, Teen Birth

Using Big Data to Study Teachers’ Impacts

School district records

2.5 million children

18 million test scores

One prominent measure of teacher quality: teacher value-added

Measuring Teacher Quality: Test-Score Based Metrics

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

Avera

ge T

est S

core

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

Avera

ge T

est S

core

Entry of Teacher

with VA in bottom 5%

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

50

36.0%

36.5%

37.0%

37.5%

38.0%

38.5%

5th Median 95th

Attendin

g C

olle

ge a

t A

ge 2

0

Effect of Teacher Quality on College Attendance Rates

Teacher Quality (Value-Added) Percentile


Earn

ing

s a

t A

ge 2

8

Effect of Teacher Quality on Earnings

$20.5K

$21.0K

$21.5K

$22.0K

5th Median 95th

12.5%

13.0%

13.5%

14.0%

14.5%

5th Median 95th

Wom

en

with T

ee

na

ge

Birth

s

Effect on Teacher Quality on Teenage Birth Rates



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


5th 95th Media

n

The Value of Improving Teacher Quality

Improving quality of elementary education can be a key

policy tool to increase upward mobility

– Teacher quality appears to be particularly important,

perhaps more so than reductions in class size

– Better teachers have roughly the same effect on long-

term outcomes from grades K-8

Education: Policy Lessons

Critical to attract and retain top talent in teaching in

primary school

– Superstar model: reward and retain talented teachers

– Higher salaries can help but ultimately may require

efforts to change professional status (e.g., Finland)

– Programs such as Teach First have been successful but

would be more effective if teachers stay

Education: Policy Lessons

“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

Is Increasing Social Mobility Desirable?

Thus far we have assumed that our objective should be

to increase social mobility

But policies that increase mobility may not be desirable

from an efficiency perspective

– Random allocation of college seats maximizes social mobility

– But violates principles of meritocracy and would likely reduce

total economic output and growth

Tomorrow’s lecture: assess tradeoff between mobility

and growth, focusing on innovation as a driver of growth

Centre for Economic Performance Lionel Robbins Memorial

Lectures

Policies to Improve Upward Mobility

Professor Raj Chetty

Professor of Economics, Stanford University

Hashtag for Twitter users: #LSERobbins

Professor Robin Burgess

Chair, LSE

Centre for Economic Performance Lionel Robbins Memorial Lectures … · 2017-07-11 · Centre for...

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