The Impact of Housing on the Wellbeing of Children and Young adults

1. The Impact of Housing on the Wellbeing of Children and Young adults David Blau, Nancy Haskell, and Donald Haurin Department of Economics Ohio State University Funded by the MacArthur Foundation

2. Motivation • There is substantial interest in knowing which factors affect child outcomes • The inputs and mechanisms are numerous • Parental time inputs (quantity and quality) • Resources received by the child (parental and public) • Family structure and composition • The child’s home environment • Homeownership, crowding, quality of the home • The child’s neighborhood environment • Most of these factors have been studied separately, but there are few/no studies of them all together • This knowledge is important for targeting public policy

3. Specific Research questions • Do child and young adult outcomes depend on (controlling for other factors): • The quality/quantity of housing a child is exposed to (includes crowding and home environment)? • Whether the parents were renters or owners of the child’s dwelling?

4. Blau & Haurin’s recent study: What role do house prices play in child outcomes? • House prices (rents and owner-occupied prices) affect many household behaviors • Quantity of housing consumed • Own or rent the dwelling • Annual savings • Choice of neighborhood • Family members’ labor supply • Family structure: marriage and divorce, fertility • Expenditures on goods • In response to capital gains on housing • Residual income left after expenditures on rents or annual costs of owning • In general, there is no specific prediction of the effect on children of house price variations in a reduced form model

5. B&H’s Findings from the reduced form model on the effect of house prices • Higher house prices are associated with reduced math cognition in U.S. children (ages 6-14) • The impact was stronger for • Hispanics • Child being age 6-10 • Very low mother’s aptitude test score • However, there are no effects of either owner-occupied or rental house price variations on children’s reading cognition or behavioral problems. • There are only a few significant effects of house prices on young adult outcomes. For example, if house prices are higher then: • Youths’ wages are lower • Being on welfare and convictions are lower

6. Literature on housing and children • Hypothesized transmission mechanisms • Crowding (Goux & Maurin-2005; Lien, Wu, Lin-2008)—affects children’s ability to study, stress level, illness, more secondary cigarette smoke • Multifamily buildings—have less play space • Lot size effects not studied • Home environment (Parcel & Menaghan-1994; Todd & Wolpin 2003)—affects child cognition • Homeownership (Green & White-1997; Haurin, Parcel, Haurin 2003)-results in better parenting skills and self-esteem, abatement of lead paint • House quality—generally not studied • Location—many studies of neighborhood effects and school quality

7. Gaps in the literature • The best known studies that find that homeownership in the U.S. has a positive effect on child outcomes do NOT control for crowding or dwelling quality • The best known studies that control for parental inputs and other factors at best control for only the home environment • It is not clear which house characteristics have significant effects on children and young adults—important for policy targeting • Very little is known about issues related to the importance of the timing of housing inputs (i.e. when during childhood) and the impact of varying durations of exposure over a child’s life.

8. Our Panel Data sources • Parental data: National Longitudinal Survey of Youth: 1979-2008: only the women’s children are interviewed • Child data: NLSY Child Supplements: 1986-2008 (we focus on 5 child outcomes) • Young adult data: NLSY Young Adult Supplements: 1994-2008 (focus on 8 outcomes) • House characteristics data • Confidential addresses from NLSY survey data were matched to publicly available data sources that described house characteristics (e.g. Zillow)

9. House characteristic data • The housing data: (single family dwellings only) • house & lot size • bedrooms, total rooms • structure type, structure age, and a quality measure • “walkability” score = a measure of the nearness of urban amenities. • We created crowding measures (bedrooms/HH size, square feet/HH size) • NLSY reports whether HH owns or rents • Survey has a measure of the internal home environment (HOME scale), which can be separated into 3 components • Cognitive stimulation • Emotional support • Quality of home’s interior

10. House characteristic data • We undertook a factor analysis and determined the set of house characteristics could be summarized in three factors • Factor 1: house size and quality: mostly a combination of square footage, number of rooms, crowdedness, and house quality • Factor 2: mostly a function of a combination of lot size, building age, neighborhood walkability. • Factor 2 is large when lot size is small, walkability is high, and dwelling age is high. (= typical U.S. urban housing) • Factor 3 is composed of the three components of the HOME scale, which we enter separately in the child outcome regressions

11. Dynamic model-child outcomes • Households maximize utility, subject to an income constraint and parental time constraints. Child quality (q) is assumed to enter their utility function. It is produced with parent’s time (T), housing (H), other purchased goods and services (G), and it is influenced by other family demographics (X). We assume a “value-added” form for the child production function, μiis a “fixed effect.” qit = θ0 + θ1Tmit + θ2Tfit + θ3Git + θ4Hit + θ5Xit + θ6qit-1 + μi + ηit + εit

12. Estimation Notes qit = θ0 + θ1Tmit + θ2Tfit + θ3Git + θ4Hit + θ5Xit + θ6qit-1 + μi + ηit + εit • The parameters are not age specific • qit-1 summarizes the effects of all inputs prior to t, there are no lagged effects of inputs • Our set of X is quite large, reducing omitted variable bias • We try fixed effect specifications for • mothers (there are multiple children of one mother in the sample), thus controlling for omitted family background characteristics • the child (panel data), thus controlling for omitted child characteristics • Unobserved factors that vary over time require an instrumental variables approach, using Ivs for the housing factors (have owners’ price, renters’ price, and a gender composition IV). • Not successful thus dropped

13. Dependent Variables: Child outcomes • Child outcomes include • PIAT Math score (cognition) • 2 PIAT Reading scores (cognition) • Behavioral Problems Index (BPI) • Body-Mass Index (BMI)—obesity

14. Young adult outcomes • The young adult outcomes are a “one-time” outcome, not a panel (no fixed effects). • We use OLS and the child’s history of the explanatory variables) • High School Degree by Age 20 • Bachelors Degree by Age 25 • Ever Convicted by Age 20 • Ever Received Welfare by Age 20 • Average Log Wages After 21 • Pregnant by Age 16 • Have children by Age 20

15. Explanatory variables • Focal explanatory variables: Housing Factors 1 and 2, whether the household is a Homeowner, and the home quality index (a scale based on interviewer observations of whether: • the play area is safe, the home is not cluttered, the home is not dark/monotonous, the home is clean, and the building is safe.) • Control variables: • Child’s age, sex, birth order • Mother’s age, race, ethnicity, marital status (6 categories), AFQT (a measure of the mother’s ability or achievement), hours worked, • House is in a central city, the number of children in each of six age-sex categories (0-4, 5-11, 12-17).

16. Additional explanatory variables that were tried • House structure type (single family, mobile home, condominium, multi-family) • Mother’s intervention in the family (used one) • Participate in school activities • Knows child’s friend’s names • Knows where child is most of the time • Quality of school index (mother’s opinion) • Proxies for expenditures on material inputs to child • Household income • Non-housing wealth

17. Child Results (Mother F.e.)

18. Child outcomes (summary) • The house size/quality/uncrowding factor has a significant positive effect on children’s math cognition and reading comprehension. It reduces behavioral problems. • The following variables have no effect on any child outcome • The housing factor indicating small lots and high walkability • Interior home quality • The home being high in cognitive stimulation improves all outcomes except BMI • High emotional support reduces behavioral problems • Other significant variables (in general): • male child, birth order, Black, Hispanic, number of children, mom’s age, AFQT, married

19. youth outcomes (OLS)

20. Youth outcomes (summary) • The homeownership variable • Increases the likelihood of High School and College degrees • Reduces convictions, pregnancy, and being on welfare • The house size/quality/uncrowding factor has a significant positive effect on college degree. It increases being on welfare (?) • The following variables have no/little effect on any child outcome • The housing urban location factor • Interior home quality • The home being high in cognitive stimulation improves degrees and wages • High emotional support improves wages and reduces convictions • Other significant variables (in general): • Net worth, income, male child, birth order, Black, Hispanic, AFQT, married

21. Comparison of child and youth outcomes • House size and the lack of crowding appears to be the most influential on child outcomes, but not on youth outcomes • Homeownership appears to be most influential on youth outcomes, but not child cognition or behavior • This result stands up even if child outcomes are included in the youth regression • An example: outcome = get a BA degree • Factor 1 has a direct positive effect • Factor 1 increases PIAT Math, which has a direct a positive effect on getting a BA • Factor 1 decreases behavior problems, which has a direct positive effect of getting a BA

22. What are the Relevant housing policies? • U.S. housing policy has had various emphasis over the past 60 years • Early policies - provide public housing, thus achieving an “acceptable” standard of quality • Successful in some cases, but not cost efficient • 1970s and early 1980s - provide housing vouchers. Again the focus was primarily on housing improving quality. • More success than public housing, but mostly rentals • U.S. housing policy, primarily in the 1990s and 2000s, was to encourage homeownership by • reduce credit constraints • introduce risk based pricing of mortgages • allow mortgage interest and property taxes to be deducted from federal income taxes • set targets for minority/low income mortgage lending for the Government Sponsored Enterprises.

23. policy implications of our results • Our results suggest support for both improving housing quality/quantity, reduced crowding, and increasing homeownership if the goal is to improve child and young adult outcomes. • However, our previous research suggested that it is NOT effective to generically subsidize either rents or the price of owner-occupied housing because there are too many other behavioral responses • Don’t subsidize large lot suburban homes • Establish housing quality targets & reduce crowding (preferably in owner-occupancy) • Encourage ownership of the property