School quality, school access and the formation of neighbourhoods

1. School quality, school access and the formation of neighbourhoods Simon Burgess and Tomas Key November 2008

2. Motivations • Understanding the role of income in gaining access to good schools. • School access – if proximity matters, how does that come about? Look at differential “strategic” moving rates. • Formation of communities – how segregated communities are formed, in relation to school quality. www.bris.ac.uk/CMPO

3. Results • Estimating the process of moving house in its possible relationship to school quality. • We show that school quality matters. • Strong differences between poor and non-poor families: • For non-poor families there is a relationship between school quality and moving; not so for poor families. • Different process for within- and across-labour market moves. www.bris.ac.uk/CMPO

4. Plan • Literature • Framework • Data • Results • Conclusions www.bris.ac.uk/CMPO

5. Literature • Results relating house price premia to school quality (Black; Machin & Gibbons). • General equilibrium models of residential location and school selection. • In the US, Epple and Romano; Nechyba; and Bayer and McMillan. • In the UK, a different setting. www.bris.ac.uk/CMPO

6. Framework • Simplified story is: • Families start out w/out children, and choose where to live on that basis • Acquire children and consider relocating before the key date for school assignment • If they choose to move, they attempt to move with increasing effort. • Of course, there are other (random) reasons for moving too. www.bris.ac.uk/CMPO

7. Framework 2 • Assumptions: • In overall equilibrium in the sense that all the distributions of income, tastes, labour market states, amenities and school qualities are fixed. • Within that, individuals move and change within a cohort as it ages. • So house prices are fixed; people move between locations, but in equilibrium, prices remain constant. • School quality and neighbourhood quality are exogenous, unaffected by the people learning or living there (future work …). www.bris.ac.uk/CMPO

8. Model • i = family (ie kid/parent); L = location • The family chooses L to maximise U(), L*. • With given supply of housing: • Bayer and McMillan, … www.bris.ac.uk/CMPO

9. Choice of L* with kids or not: • Pick L*(0) to start with at k=0 so q is irrelevant. So necessarily live somewhere nicer in terms of e and/or cheaper. • L* (k=1) cannot be at a lower q than L*(k=0), unless e is correlated in strange way. • The decision whether to move at all or not is balancing the extra cost of higher price Dp, with the value of higher quality, lDq. www.bris.ac.uk/CMPO

10. Invest in attempting to move, c. • So pia = f(c*), and c*= f(a, DUia), where Dbetween k = 0 and k = 1. • Approximate: www.bris.ac.uk/CMPO

11. So, within TTWA Dm = 0: • Dp* substituted out by Dq, De and location. • Allow for heterogeneity in response to q • Include q or Dq? www.bris.ac.uk/CMPO

12. Data • PLASC/NPD • 5 censuses merged together • Non-selective, non-middle schools LEAs • Looked at TTWAs as unit, LEAs. • Spatial controls: • TTWA dummies, LEA dummies • LLSOA dummies • Smoothed LLSOA effects from contiguous areas www.bris.ac.uk/CMPO

13. Primary School Secondary School Year 1 Year 5 Year 6 Year 7 S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A Census Census Census KS2 Census Apply for SS: need ‘good’ p’code here Moving house here could be strategic P’code changes here could be realisations of coding errors Timeline www.bris.ac.uk/CMPO

14. We have tried several different controls for spatial context: - Dummies for Travel To Work Area - Dummies for Lower Layer Super Output Area (LLSOA) - Smoothed LLSOA effects from contiguous areas, using the IMD Score for neighbouring LLSOAs, as well as the pupil’s own IMD score. • Varying these controls has no qualitative impact on the results. www.bris.ac.uk/CMPO

15. Two different ways of defining ‘default’ secondary school: • Nearest secondary school • Modal secondary school given primary school attended • Lots of cleaning work on changing postcodes, to eliminate redistricting, input errors and mis-coding • We attempt to identify siblings in our data, and pick out eldest mover only. We do this by grouping pupils who move from/to the same postcode, and count these pupils as a family if there are less than 8 of them. www.bris.ac.uk/CMPO

16. Cleaning of p/code changes • We use Royal Mail information about postcode redistricting. • We do not count as moves postcode changes that leave the first and last two characters of the postcode unchanged. • If all in former postcode moved, and all includes more than 8 pupils (our cut-off for a family), then we do not count this as a move: it is likely to have been a redistricting. • We do not count moves of less than 100m. • We do not count as a move cases where either of the first or last two characters of the postcode only change. • We do not count as a move cases where the first or last two characters only are coded in reverse compared with the postcode for the other academic year. • We do not count as a move cases where there are changes in the postcode length by one character, e.g. AB1 becoming AB12 or B12 becoming CB12, with all remaining characters unchanged. www.bris.ac.uk/CMPO

17. Results • Descriptive analysis of moves • Basic analysis of probability of moves • Analysis by within- and across-TTWA moves • Analysis by pupil age • Dynamic, non-linear panel data models with unobserved heterogeneity and initial conditions problems. • Panel analysis 1 • Panel analysis 2 www.bris.ac.uk/CMPO

18. Summary Statistics www.bris.ac.uk/CMPO

19. Moves 1 www.bris.ac.uk/CMPO

20. Moves 2 Pre-move GOR (Note eg Post-move GOR, London – 9286m) www.bris.ac.uk/CMPO

21. Changes in School Quality www.bris.ac.uk/CMPO

22. Changes in Neighbourhood Poverty Note – negative means a fall in the IMD, so an improvement www.bris.ac.uk/CMPO

23. Basic Results www.bris.ac.uk/CMPO

24. Within- and across-TTWA moves www.bris.ac.uk/CMPO

25. By age www.bris.ac.uk/CMPO

26. Econometric Issues • Potential problems: • Omitted variables? Neighbourhood – well covered; schools – GCSE results, other things likely correlated; Families – repeated obs. • Reverse causation? Timing, local controls, movers are small fraction of any school. • Initial conditions problem: current school quality might result from previous choices – a (non-random) group of families may already have moved. www.bris.ac.uk/CMPO

27. Main threat to identification: Birds of paradise behaviour (Some) birds of paradise build a nest first and then seek a mate – not least through having a nice nest to live in. The equivalent here is families moving to get a good default school before having a child to send to the school. If pre-kid location chosen independent of school quality, then in principle this is ok as an initial condition. Separate practical problem that we don’t see everyone from the start. www.bris.ac.uk/CMPO

28. Econometric Issues • Assume q (age = 0) is exogenous, we first see people at age = A. Some movers may already have moved by then. • Who would move early? People with high preference for schooling, low preference for other amenities • So expect a bigger coefficient if capture more and earlier part of families’ lives. Confirmed: get bigger coefficient on short early window, and on long window. www.bris.ac.uk/CMPO

29. Panel Analysis • Use Wooldridge’s approach for dynamic nonlinear models with unobserved effects and initial conditions problems. • Approach models the distribution of the unobserved heterogeneity conditional on the initial value. • Essentially a random effects probit model with controls for initial state. www.bris.ac.uk/CMPO

30. Panel data model 1 www.bris.ac.uk/CMPO

31. Panel data model 2 www.bris.ac.uk/CMPO

32. Conclusions • Implications for school access. • Implications for the formation of neighbourhoods. • Invert estimated moving model to analyse the composition of neighbourhoods. • Future work: … joint model of school performance and neighbourhood formation www.bris.ac.uk/CMPO