“A Unified Framework for Measuring Preferences for Schools and Neighborhoods”

Bayer, Ferreira, McMillian

Research Question

• How to measure households value for good schools and neighborhood characteristics?

• Why do we care?– School quality affects economically important

outcome like earnings (important topic in labor economics)

– Public policy: property taxes fund education, policy evaluation e.g. cost benefit analysis of desegregation programs

Literature Review

• Black (QJE, 1999)-Typical approach look at effect of school quality

on test scores and earnings

-Alternative approach: estimate households willingness to pay for better school

• Basic idea: when agent purchases a home, she is also pay for:– Type of house she buys – the schools that her children go to– Neighborhood characteristics

Willingness to Pay

• Hedonic Model:

– X- characteristics of house e.g. size, type, # rooms– Z- neighborhood socio-demographics– ε – error term

• ID problem: endogeneity of neighborhood characteristics

• Solution: Boundary Discontinuity Design– Instrument for socio-demographics

Boundary Discontinuity Design: Ideal Experiment

School Attendance Zone A

School Attendance Zone B

Boundary Discontinuity Design

• Socio-demographics of neighborhoods the same

• Difference in Quality of school depending on school attendance zone paying for school quality

• In practice, need to consider housed in narrow bands (0.1-0.3 miles)– Statistical Power to make inferences

• Need to control for socio-demographics

Ownership and # of Rooms

Test Scores and Housing Prices

Contributions

• Addresses endogeneity of neighborhood characteristics– Produced more consistent estimates of willingness

to pay for good school• Limitation of Study– Does not control for socio-demographics above on

beyond boundary instrument

Bayer, Ferreira, McMillian

– Improve on Black by• Using richer data set

– Unrestricted Census Data» Contains block level information

• Embedding Boundary Discontinuity Design within discrete choice heterogeneous sorting model

Data• Decennial Census -- restricted version (1990)

– Filled out by 15% of households– Individual Level Data: race, age, education attainment, income of each household member, type of

residence: owned, rented, property tax payment, number of rooms, number of bedroom, types of structure, age of building, house location, workplace location

– Neighborhood level data: race, education, income composition, also add data on crime, land use, topography, local schools

– matched with county level transactions data, matched with HMDA data • to get 60% of home sales and neighborhood variables for 85%

• Relevant Study Sites: Area: Bay Area: Alameda, Contr Costa, Marin, San Mateo, San Francisco, Santa Clara• Advantages:

– small area, ppl don’t typically commute out of area– lots of data:

» 1,100 census tracts, 4,000 census block groups, 39500 census » full sample 650k people, 242.1k households

• School quality measure: avg. 4th grade math and reading score – Advantage: easily observable to both teachers and parents

Summary Statistics

• Home value $300,000• Rent $750/month, • 60% homes owned, • 68% black, 8% white, • 44% head of households college degree,• avg. block income $55,000

Implementing BDD

• Each census block assigned to closest school attendance zone boundary

• Each block paired with a “twin” census block– Closest block on opposite side of boundary

• For each pair, block with lowest average test score designated “low” side of boundary, the other “high” side

• Boundary Cutoff: census blocks ≤ 0.2 miles from nearest (SAZ)– Have power to restrict even further to ≤ 0.1 m

BBD Continuous Observations

• Housing Characteristics that are continuous across the boundary:– Number of rooms– Construction date– Ownership status: owner occupied/rented– Size: lot size, square footage

Construction Date and Size

BBD Discontinuous Observations

• Housing Characteristics that are discontinuous across the boundary:– House Price (by $18,719 , i.e. 7%-8% of mean value)

• Neighborhood Characteristics that are discontinuous across the boundary:– Test Scores (by 74 pts)– Percentage Black (by 3%)– Percentage with College Degree (by 5%)– Mean Income (by $2,861, i.e.6%-7%)

Education, Income & Race

Conceptual Take Away

– Quality of physical housing stock same across boundary

– prices different – socio-demographics – and test scores different– Inference: households on the “high” side of the

boundary paying for higher quality schools and sorting into the SAZ with better schools

Hedonic Price Regression

Comments

• Accounting for Boundary Fixed Effects Reduces hedonic valuation of good schools– Consistent with Black (1999)

• Controlling for Neighborhood Socio-demographics reduces it further

• Households racial preferences for neighbors not capitalized in housing prices– Coefficient on percent black drops from -$100 to

almost zero with Boundary fixed effects

Robustness Checks

• School level socio-demographics– Race, language ability, teacher education, student income– estimate on preference for school test score in baseline:

17.3 (5.9) – with addition control estimate: 22.6 (8.5)

• Inclusion of Block-level socio-demographics• Dropped Top Coded Houses in Census Data (with values greater than

$500,000) • Use housing prices from transactions data• Using Only owner occupied units• Take-away: results robust to those in base-line specification w/o these

detailed measures

Discrete Choice Sorting Model

• Model– Each household (i) decides which house (h) to buy/rent– Random Utility Model (McFadden)

• House characteristics (Xh) – size, age, type)– Type (owned/rented)– Neighborhood and School characteristics

• Distance from house to work (d ih)

• Boundary fixed effects (Θbh)

• Price (ph)

• Unobserved housing quality (ξh)

• Individual specific error term (εih)

Maximization Problem

• Objective:

• Allow for agents valuation of housing characteristics to depend on individual characteristics:

Estimation Strategy

• Two step process– Separate utility function into part that captures mean

preferences and part that captures preference heterogeneity

– Step #1: Use MLE to estimate heterogeneous parameters and mean utility

– Step #2: Separate mean utility in components that are observable and unobservable• Utilize assumption that Individual specific error term (εi

h) follows extreme value distribution

• Use characteristics of houses > 3miles away as price instrument to obtain causal estimates

Results

Comments

• Preferences for better schools similar across hedonic BDD estimates and discrete choice model

• Preferences for black neighbors highly negative in discrete choice model estimate– Different from hedonic estimation for race

preference– Idea: self-segregation by race can arise through

sorting that does not affect equilibrium prices

Robustness Checks