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“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?

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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
contributions
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
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
slide11
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
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
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%)
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
comments
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
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
comments1
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
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