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

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Presentation on theme: "School quality, school access and the formation of neighbourhoods Simon Burgess and Tomas Key November 2008."— Presentation transcript:

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

2 PLUG, Nov 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.

3 PLUG, Nov 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.

4 PLUG, Nov Plan Literature Framework Data Results Conclusions

5 PLUG, Nov 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.

6 PLUG, Nov 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.

7 PLUG, Nov 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 …).

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

9 PLUG, Nov 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 and/or cheaper. L* (k=1) cannot be at a lower q than L*(k=0), unless is correlated in strange way. The decision whether to move at all or not is balancing the extra cost of higher price p, with the value of higher quality, q.

10 PLUG, Nov Invest in attempting to move, c. So ia = f(c*), and c* = f(a, U ia ), where between k = 0 and k = 1. Approximate:

11 PLUG, Nov So, within TTWA m = 0: p* substituted out by q, and location. Allow for heterogeneity in response to q Include q or q?

12 PLUG, Nov 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

13 PLUG, Nov Timeline Year 1 Year 5 Year 6 Year 7 Primary School Secondary School S O N D J F M A M J J A Census KS2 Census Apply for SS: need good pcode here S O N D J F M A M J J A Census Moving house here could be strategic Pcode changes here could be realisations of coding errors

14 PLUG, Nov 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 pupils own IMD score. Varying these controls has no qualitative impact on the results.

15 PLUG, Nov 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.

16 PLUG, Nov 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.

17 PLUG, Nov 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

18 PLUG, Nov Summary Statistics

19 PLUG, Nov Moves 1

20 PLUG, Nov Moves 2 Pre-move GOR (Note eg Post-move GOR, London – 9286m)

21 PLUG, Nov Changes in School Quality

22 PLUG, Nov Changes in Neighbourhood Poverty Note – negative means a fall in the IMD, so an improvement

23 PLUG, Nov Basic Results

24 PLUG, Nov Within- and across-TTWA moves

25 PLUG, Nov By age

26 PLUG, Nov 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.

27 PLUG, Nov 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 dont see everyone from the start.

28 PLUG, Nov 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.

29 PLUG, Nov Panel Analysis Use Wooldridges 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.

30 PLUG, Nov Panel data model 1

31 PLUG, Nov Panel data model 2

32 PLUG, Nov 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

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