A unified framework for measuring preferences for schools and neighborhoods
This presentation is the property of its rightful owner.
Sponsored Links
1 / 27

“A Unified Framework for Measuring Preferences for Schools and Neighborhoods” PowerPoint PPT Presentation

  • Uploaded on
  • Presentation posted in: General

“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?

Download Presentation

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

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript

A unified framework for measuring preferences for schools and neighborhoods

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

Bayer, Ferreira, McMillian

Research question

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

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

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

    Boundary Discontinuity Design: Ideal Experiment

    School Attendance Zone A

    School Attendance Zone B

    Boundary discontinuity design

    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

    Ownership and # of Rooms

    Test scores and housing prices

    Test Scores and Housing Prices



    • 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

    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

    A unified framework for measuring preferences for schools and neighborhoods


    • 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

    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

    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

    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

    Construction Date and Size

    Bbd discontinuous observations

    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

    Education, Income & Race

    Conceptual take away

    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

    Hedonic Price Regression



    • 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

    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

    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 (dih)

        • Boundary fixed effects (Θbh)

        • Price (ph)

        • Unobserved housing quality (ξh)

        • Individual specific error term (εih)

    Maximization problem

    Maximization Problem

    • Objective:

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

    Estimation strategy

    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 (εih) follows extreme value distribution

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





    • 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 checks1

    Robustness Checks

  • Login