“a unified framework for measuring preferences for schools and neighborhoods” bayer, ferreira,...
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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?– 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
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