Everybody Needs Good Neighbours?

Posted by Steve Gibbons, LSE and SERC

New neighbours have moved in to your street. Should you worry about the effect their children will have on yours? Will they be a good influence or bad? If you had read a lot of the past research on so called ‘neighbourhood effects’ you might be very worried. These studies often show a correlation between the kind of neighbours a child grows up with, and their subsequent behaviours and educational achievement. These findings have been very influential, for example in motivating policy to encourage mixed communities.
 

However, a study by SERC researchers Dr Steve Gibbons, Dr Olmo Silva and Dr Felix Weinhardt published in this month’s issue of the Economic Journal (also here) , tells a very different story. It turns out that changes in neighbours make no difference at all  to how well children already living in the neighbourhood do in tests at school. Neighbours seem to make some difference to a child’s attitude to school and their propensity for anti-social behaviour, but even here the effects are very weak. 

The study looks at the effect of neighbouring, similar-age children on a child’s school test scores and on other behavioural outcomes. It investigates how these test scores and behaviours change over time, as other children move in and out of a child’s home neighbourhood and change the composition of the child population in the local area. It looks at changes in the mix of boys and girls,  the average ability (measured by early age 7 test scores) and whether or not they are on free school meals (a standard proxy for low income). The investigation is carried out using a big administrative data set of over 1.3 million teenagers in England, who can be tracked for up to five years.

So why do the findings differ from earlier work? The reason is that people choose where to live, subject to their incomes and the cost of housing. This point was made in an earlier SERC blog. The correlation between children’s outcomes and neighbours’ characteristics comes about mainly from the fact that the children from richer families live next to other children from rich families, and children from poor families live next to other children from poor families. And children from rich families tend (on average) to do better at school. Neighbours’ school test scores are also correlated with each other because children in the same neighbourhood attend the same schools. 

Researchers can use statistical methods to try to ‘control’ for these differences using data on income and school quality, but this approach always has limited success. In contrast, this latest study, by looking at what happens to a given child as their neighbours move in and out over a number years, is able to circumvent the worst of these problems.

These research findings do not stand alone in this respect. The best evidence emerging from the US and elsewhere using experimental methods (e.g. the Moving to Opportunity experiment) or other experiment-like research designs comes to similar conclusions. The quality of your neighbours, good or bad, does not make any difference to your child’s education, or other outcomes related to economic self-sufficiency. Neighbours may, on the other hand, matter for physical health and mental wellbeing – but as yet there's limited evidence on this for Britain.

Labour's Housing Policies

[Posted by Prof Henry G. Overman]

Swamped with plans for new What Works Centre for Local Economic Growth plus policy book summarising lessons from first five years of SERC research - so not much time for party political conferences. However, a number of people have asked for my take on Labour's conference commitments on housing. Here's my first pass, building on Randeep Ramesh's helpful summary in the Guardian.

Setting aside the issue of whether 200k per year is enough (and more or less than previous commitments) my thinking runs as follows:

1) Tackle land hoarding.

Fine in principle, assuming we are talking pure speculative activity. I'm not sure how big a problem it is, however.

If we are talking about land with permissions my back of the envelope calculations from earlier in the year suggest a 400k figure is highly misleading: "Unimplemented units are either unstarted or under construction. Of the 399,816 headline figure 37% are unstarted (down from 47% in 2008). Units under construction make up the other 63%. In terms of numbers of units that equates to around 150k units un-started, around 250k under construction." (which squares with a more recent 200k figure coming from the National Home Builders Federation).

That's clearly not enough for the 1.4m homes that could go on 'strategic land bought with options' (the Guardian says that figure comes from Ed Milliband's office). That must mean that most of these options are for land without permission. You buy this land because it's cheap now but would be worth a lot with planning permission. Once it gets planning permission you have to build on it to realise those gains. What you don't do is buy expensive land with permission and sit on it waiting for prices to rise. How do we know this? First, because that would be counted in the 150k figure above (and it's not the only source of delay - much of that will be for developers that went bust). Second, because it doesn't make much sense to hold land with permission just because of general land price inflation (again, the 150k figure confirms this). All of this might suggest that Ed Milliband's office doesn't understand much about planning gain.

2) Give urban centres the right to grow

In other words, force the shires to take houses that they don't want. This could help (although it would be a return to the kind of top-down planning that Labour used during the 2000s) although it sure as hell won't be popular!

3) Create development corporations

According to the Guardian these "would seek cheap agricultural land to buy and build on – and use the profits from the sale of houses to repay the investment. The attraction for Labour of these corporations is that any borrowing stays off the government's balance sheet." This one is almost funny. Cheap agricultural land is land without planning permission, once it has planning permission it may still have plants growing on it but it's not agricultural land and it's certainly not cheap. For example, the latest 2011 VOA report suggests agricultural land in Oxfordshire is worth £20,995 per hectare; with planning permission for residential it's worth £4m (i.e. 200 times as much). So, if this is really what Labour is planning the trick is to buy agricultural land where the LA will not grant permission and then over-rule the LA, grant central government planning permission and build housing on it. You could, of course, achieve the same thing by getting rid of LA control over land planning, take the same land, grant permission and impose restrictions on the private sector developer that you then sell that land to (at £4m per ha). The beauty of this has nothing to do with whether or not it's on the government balance sheet. It is, however, going to require removing LA control over the land planning process and handing this to top down central government planners. To put it modestly, that's quite a big step.

I'd need more time for a proper analysis - and as I said the underlying interpretation of Labour policy came from the Guardian article linked above - but still doesn't look particularly convincing.

Politicians and Housing

[Posted by Prof Henry G. Overman]

As the housing crisis continues, politicians continue to say silly things about housing.

Here's a round up of three efforts from the middle of last year. I was reminded of this by Harriet Harman on the Today Programme this morning. Talking about improving housing affordability she was asked if she would like to see housing prices fall. This is, of course, a tricky question for a British politician - and she dodged it by saying that house price falls would be bad but that Labour were committed to increasing supply and improving affordability.

Unfortunately, this really doesn't make any sense. 'Affordability' is essentially determined by the price of housing relative to incomes and the availability and cost of mortgage finance. As we know, availability is an issue for some buyers at present. Fixing this would improve the situation for those buyers but, in the absence of any increase supply, would raise prices overall. This is the central problem with the government's help-to-buy scheme. The cost of finance is at a historic low and you would put your money on that cost increasing rather than decreasing in the medium to long run. We would, of course, all like incomes to rise but general increases in income will also raise prices overall in the absence of a supply response. Fixing affordability requires housing poor families to see their incomes rising faster than housing rich families. Desirable, perhaps, but I'd like to know how that's going to be achieved in practice.

In short, fixing the general affordability problem requires government to do something to get house prices to fall relative to incomes. With incomes growing slowly, this will require absolute falls in house prices unless we are willing to wait a very long time for housing affordability to improve. Falls in house prices (absolute or relative) will require increased supply. This, in turn, is a key component of labour's housing policy (they are pledging to build 200,000 more house). Setting aside whether that policy will be effective in increasing supply - something for another day - to be effective in improving affordability it will have to lead to falls in house prices. The Coalition is trying to achieve exactly the same thing through some of it's planning reforms.

Of course, it would be a brave politician who acknowledged that they want to increase supply sufficiently to see prices fall - because existing owner occupiers stand to lose if prices fall. In turn, this is the reason why home owners tend to oppose new building which constrains supply and causes affordability problems. There are no easy answers to this policy dilemma, but unfortunately denying that it exists is unlikely to get us very far in solving the housing crisis.

The Regional Economic Impacts of HS2

[Posted by Prof Henry G. Overman]

In yesterday's blog post I was highly critical of the latest HS2 report on regional economic impacts. That post focused mainly on the fact that the report makes a technical error that makes its estimates of the productivity effects of HS2 impossible to interpret. As these underpin the entire report that is, to put it mildly, a bit worrying.

I also suggested that I thought the scale of the impacts was out by some order of magnitude. As most people are likely to be interested in the answer rather than the method, I thought I would give a little more explanation on why I think that's the case.

As I explained in yesterday's post, to understand the economic impact of HS2 we need to know how changing the connectivity between places affects local economies. The starting point for figuring this out is to recognise that better connected places have higher productivity (i.e. they can produce more goods and services with less resources). The report uses statistical analysis to make this relationship more precise. To do this, it takes data on wages in 235 local areas and data on the connectivity of those places and looks at how closely those things move together. In technical terms, it runs a regression of a wage-based measure of productivity* on transport connectivity (controlling for some other things that you might think would influence both productivity and connectivity). Together with colleagues, I have used a similar approach for the Northern Way and there is an academic literature that has applied these approaches more widely, albeit in different contexts.

In our work for Norther Way, we looked at the impact of a 20 minute reduction in travel time between Leeds and Manchester. We find that closer integration between Manchester and Leeds could be expected to have a positive effect on wages. Our largest estimate, for a 20 minute reduction in train journey times between Leeds and Manchester, has average wages increasing by between 1.06% and 2.7%.

These numbers seem larger than those for HS2 (which suggest a 0.8% uplift for national GDP). Two points to note. First, these effects were only for 23 Local Authorities most directly effected (the 1.06% is for Tameside, the 2.7% maximum for Wakefield). Second, these were our largest estimates. As we go on to explain in the report: "It is important to note that nearly all of these wage effects come about because the composition of the workforce is different in larger, better connected places. This difference in composition may arise through sorting effects – movements in the population in response to economic opportunities, or because people change their characteristics in response to changes in labour market size. It is also possible that the difference could arise because of past decisions to better connect existing concentrations of more productive workers. Whatever the mechanism, the research identifies a particularly important composition effect, in terms of the years of education of the workforce and an even stronger role for unobserved characteristics of worker such as, for example, cognitive ability. The effects for an individual worker, with given and unchanging characteristics (often called place-based effects), are smaller at somewhere between 0.20 and 0.50 of a percent."

This kind of reduction in the effect of connectivity, once we control for composition, is consistent with the academic literature that has considered these issues. In contrast, the procedure that the HS2 report uses to 'correct' for skills has almost no effect on the point estimates. This is, perhaps, not surprising given the quality of the data that the report is using - but it is very worrying (and the HS2 report uses the uncorrected estimates anyhow).

This doesn't of course, tell us which set of estimates we should prefer. To think about this, it's probably worth quoting our Northern Way report at length:

"So what, then, do the larger estimates – those that do not remove the effect of composition – tell us about the likely effect of increased integration? These estimates are best thought of as an upper bound to the overall economic impact of improved linkages. They represent the combined impact of (i) place-based
productivity effects on workers with fixed characteristics (ii) movement and sorting of more productive workers into more closely connected places and (iii) upgrading of education and skills for workers. They capture the effects of place, plus sorting, plus education and skill upgrading, once workers have moved around across places in response to reduced transport times and the greater integration that delivers. But caution about this interpretation is required, as the direction of causality may not run from improved connectivity to labour market composition, but in the opposite direction: Productive labour markets may encourage better transport linkages. If this is the case then improving transport linkages will not be effective in changing the composition of the labour market, preventing the realization of overall effects on the scale implied by the higher estimates.

The smaller estimates, taking out the effects of composition, capture the additional benefits to workers who don’t enhance their skills or become more educated or able in response to economic changes that occur as a result of improved transport links between Manchester and Leeds. They are also less sensitive to the possibility, outlined above, that transport policy has no effect on labour force composition. For these reasons, and because the effects, net of composition, capture the beneficial impact on individuals, many economists argue that they represent the most appropriate focus for policy. Arguably, the smaller estimates are likely to be the most relevant for the vast majority of people currently working in the two city regions.

Therefore, the role of these findings in the assessment of particular investment propositions will depend on policy objectives. A traditional cost-benefit analysis should exclude the impact on wages generated purely by the sorting of individuals from one place to another. However, the upper-bound, combined impacts (including composition) might be of more interest to some policy-makers. They may be seen as appropriate objectives by sub-national authorities working at regional or local levels aiming to increase average wages or incomes in their specific areas, or - as in the UK – where national government has adopted objectives to address aggregate spatial disparities per se."

In short, you should be using the smaller numbers to think about the national impact and the larger numbers to think about the local impact. Even then, as pointed out by Robert Peston, all that skill upgrading, private capital investment etc is not free, so it is totally incorrect to count this as a benefit of HS2 without thinking about the additional (private and public sector) costs of achieving those changes.

But even those smaller numbers are misleading when thinking about the national impact of HS2, because the change we were modelling - a 20 minute reduction in travel time between Manchester and Leeds is likely to have a much bigger impact on connectivity than HS2. Indeed, according to our way of calculating connectivity (which is similar, but not identical to that used in the HS2 report) the connectivity impact of 20 minutes off Manchester-Leeds was 3 times that of a 40 minute reduction for Leeds-London.

In short, our modelling of a large local connectivity change for Manchester and Leeds gave estimates of a local economic impact of between 0.2% and 0.5%. Of course, our analysis isn't perfect. But it is based on better data and is (I hope) technically correct. It produces results that are consistent with the academic literature and that seem proportionate to the scale of the project that we were modelling. HS2 will bring some regional economic impacts and they should be counted in the benefit cost case. But on the basis of available evidence they will almost certainly not be anything like 0.8% of national GDP.

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* Correction 05/11: The KPMG report uses an output-based measure of productivity, not a wage-based measure on the left hand side of its estimating equation. That doesn't change the basic point I am making here about the problems of overestimation if skill composition (and other confounding factors) not controlled for properly.

HS2 Regional Economic Impact: Garbage in ...?

[Posted by Prof Henry G. Overman]

I have finally found time to look at the technical appendix for the HS2 Regional Economic Impact report that emerged yesterday and underpins the widely cited '£15bn a year' benefit claim.

I'm going to preface my comments here by observing that modelling this kind of regional economic impact is very difficult. That said, assuming I have understood it correctly, this report does things that are technically wrong (and these things are crucial for the analysis).

To understand the economic impact of HS2 we need to know how changing the connectivity between places affects local economies. The starting point for figuring this out is to recognise that better connected places have higher productivity (i.e. they can produce more goods and services with less resources). The report uses statistical analysis to make this relationship more precise. To do this, it takes data on wages in 235 local areas and data on the connectivity of those places and looks at how closely those things move together. In technical terms, it runs a regression of a wage based measure of productivity on transport connectivity (controlling for some other things that you might think would influence both productivity and connectivity). You might worry that more productive places end up with better transport links - i.e. there's a chicken and eggs problem here. Let's set that issue aside (as the report does nothing to address it and the literature suggests this is not as important as you might think). Once we know the relationship between productivity and connectivity, we can model how HS2 changes connectivity and hence back out the effect on productivity. So far, so good (this is something I have done in the past for the Northern Way).

The report wants to do something more ambitious by looking at how different types of connectivity affect productivity in different industrial sectors. To do this, it constructs four different measures of connectivity - via the rail network to workers and jobs and via the road network to workers and jobs. The problems start because these four measures of connectivity are very highly correlated (i.e. they tend to move together) so that places with good road connectivity also tend to have good rail connectivity. In fact, they are sufficiently highly correlated that its very difficult to figure out which of them matters. To 'solve' this problem the report looks at each of the correlations in turn - asking how does productivity change with rail connectivity to workers, then with rail connectivity to jobs etc. Unfortunately, as we teach our undergraduate students, this doesn't actually solve the problem. It just attributes the effects of all the different types of connectivity to the particular type of connectivity you are looking at. It's a little hard to understand what they do next if you've never done any basic statistical analysis (and also because the report is a little unclear). Let me give the technical explanation - I've tried to give a non technical analogy at the end of this post.

From a technical point of view they run four univariate regressions of productivity on the four different connectivity measures to give estimated coefficients b1, b2, b3 and b4. They then sum these coefficients to give a 'total' of the estimated coefficients (b1+b2+b3+b4.) Next they take the largest coefficient (say b3) and assume that the effect for each connectivity measure can be calculated using its share in the total coefficients multiplied by b3 (the largest estimated coefficient. So, the coefficient for the first connectivity measure can be calculated as b3 * b1/(b1+b2+b3+b4); for the second connectivity measure it can be calculated as b3 * b2/(b1+b2+b3+b4), etc. The report notes that 'this approach does not have a firm statistical foundation'. This is an understatement - this procedure is essentially unfounded and produces estimates of effects that are meaningless. The only world in which this would make sense is one in which the connectivity measures were perfectly correlated so each of them got the same estimated coefficient and you were going to pretend that you could separate out the effects for four different changes (which of course, you can't do).

We had the same problem in work we did for the Northern Way. However, unlike the HS2 report, we present both multivariate and univariate coefficients (for two, rather than four, connectivity measures - one for the car and one for train). In our first set of univariate regressions the coefficient for car connectivity is 0.230, that for train connectivity is 0.344. Adopting the HS2 approach, we would calculate 'corrected' coefficients on car connectivity as 0.137 [=0.344 * 0.230/ (0.230+0.344)] and one for train connectivity of 0.206 [calculated similarly]. Neither of these turn out to be correct. In the multivariate regression, the correct way to calculate the partial effects these coefficients are 0.084 and 0.258 respectively.

In short, it's hard to know how to interpret the coefficients in the report. That's made harder by the fact that the paragraph of the report that explains how to interpret these coefficients [6.3.38 for those of you that want to take a look] is unfinished - '[this procedure] enables connectivity to other businesses and to labour, by car and by rail, to be reflected in the analysis and captures.' Personally, I think that sentence is completed by the word 'nothing' but I suspect not what the authors intended.

Issues of interpretation aside, it's as worrying that, in our analysis for the Northern Way the coefficient on train connectivity reduces by a factor of 6 to 8 once we properly account for other things that might be both driving productivity and accessibility. In the HS2 report, the one thing they do to address this problem is a (somewhat opaque) correction for skills which, worryingly, hardly changes the estimates.

So, on my reading, technically wrong and possibly out by orders of magnitude. I can imagine why the government has rushed this report out, but it would appear to add very little, if anything, to the debate.

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Non technical analogy: I've been trying to come up with an analogy that conveys just how wrong this is to someone who is non-technical. The best I can do is to think about trying to understand net daily calorie intake. We know that both calories in (through eating) and calories out (through vigorous exercise) affect net daily calorie intake. We know that a 1% increase in the amount eaten will have a much bigger effect (lets say a 10% increase in weight) than a 1% decrease in the amount of vigorous exercise taken (lets say a 1% decrease in weight) because baseline activity requires a couple of thousand calories a day even if we are not taking any vigorous exercise. This simple relationship is the reason why increasing daily exercise by some percentage is not as effective in weight loss as decreasing calories consumed by the same percentage. If we looked at the association between weight and daily diet, ignoring exercise taken (i.e. did a univariate regression) - we might find that people with 1% higher daily calorie intake have 11% higher weight. If we then looked at the association with exercise (ignoring calories consumed) we might find that those that exercise 1% less have a 9% higher weight. That is, we estimate effects that are larger than the true effects - a little higher for calories, a lot higher for exercise. The problem, of course, is that exercise and calorie intake tend to be quite highly correlated, because people who exercise a lot tend to watch what they eat and vice versa. This causes us to overestimate the effect of calories consumed (because high calorie consumers exercise less) and significantly overestimate the effect of exercise (because low exercisers also eat more). To figure out the effect of exercise on its own we really need to vary the amount of exercise holding the amount of food intake constant - i.e. do a multivariate regression. This would give us the 1% effect that I assumed above and a 10% effect for obesity. Instead, if we followed the approach adopted in this report we would sum the two coefficients to give 20%, take the larger coefficient of 11% and work out the coefficient on of exercise on weight loss as 4.95% [11% x (9%/20%)] and that on calorie intake as 6.05% [11% x (11%/20%)]. 

Requiem for Detroit

[Posted by Prof Henry G. Overman]

BBC4 was showing Requiem for Detroit last night and I caught most of it (for the first time). As always with Detroit, the scenes of urban decay were incredible (Daniel Knowles referred to it as 'ruin porn' - which sort of captures it). Also, the stories about individual impacts were an interesting mixture of the very depressing and the truly inspiring.

Such images and stories encourage strong emotions. I sometimes wonder whether this tends to generate an in-built bias when thinking about urban decline and the appropriate policy response. Specifically comparing Detroit now to Detroit at the peak of its boom encourages us to think that policy could have and should have stopped the massive population decline that led to so much urban decay. But what if this wasn't possible? What if pouring billions of dollars in to Detroit couldn't have stopped that decay? Then the appropriate comparison is not to Detroit at the peak of its boom but to a Detroit with a larger population but still precious few jobs. Would this be better?

Another way of looking at this is to note that such documentaries hardly ever follow the fortunes of the hundreds of thousands of families who move away from the declining city. What happened to them? Did their lives end up better or worse than they would have if they stayed put? Of course, it's always hard to know. But focusing only on those left behind presents a very one sided view of the full impact of the decline of a once great city on all its residents past and present.