Movin’ On Up: Happiness and Urban Economics

Imagine thinking about where to live. Imagine thinking about any decision really. That decision will be based on forecasts of your well-being and the well-being of those that you care about. Well-being comes in many guises and will be affected by a range of factors, such as how rich and healthy you are. Sometimes income and health are used as measures of well-being in their own right. To us, well-being is best represented in subjective terms: in essence, by reports of happiness. We use the term ‘happiness’ very broadly not only to capture good moods but also to give a sense that life is going well.
There is now an accumulation of evidence to suggest that we are often not very good at making choices that make us happier. We are often guilty of ‘miswanting’: of not wanting most the things we will enjoy best. This has important implications for how we think about many things, including urban density and policy.
Take the decision about how far to live from work. Most of us will think about the trade-off between a longer commute and a bigger house in a less expensive area. The trouble is, we seem to make the wrong trade-offs, at least so far as happiness is concerned. For example, activity-based reports of happiness in the US and in Germany have shown commuting to be one of the least pleasurable activities during the day. Longitudinal data in Germany have been used to show that commuting over two hours a day negatively impacts on life satisfaction, controlling for factors such as income and housing quality. Interestingly, similar data from the UK suggest that women are more likely to be negatively impacted by commuting than men.
Consider a different example and a different type of study. First-year college students in the US were asked to forecast what their overall level of happiness would be the following year if they lived in various dormitories at their university. The forecasts gave much greater weight to physical features of the dorms, such as location and house, over social ones, such as relationships with roommates and sense of community. A year later, after the students had been randomly assigned to one of the dorms, their happiness was found to have been determined much more by the social features of their dorm than by its physical ones. The students had made a forecasting error.
Forecasting errors are quite common and we have a pretty good idea about why they come about. When you are making a choice between two houses, for example, you will focus attention on what makes those houses different and in particular on observable characteristics of difference, such as the number and size of rooms. We like to have clear reasons for why we made a particular decision. This ‘lay rationalism’ means that it is much easier for us to justify – to ourselves and to other people – a house purchase based on the size of the house.
Those observable and justifiable characteristics that draw attention to themselves at the time a decision is made may not be – and indeed are often unlikely to be – the characteristics that draw attention to themselves in the experience of that decision. In the experience of our house, for example, we are more likely to be affected by factors such as how well we get on with our neighbours and by traffic noise.
It may be the case that our happiness is impacted upon by the size of the house in the first few days or weeks, but we quickly adapt to such things as we withdraw attention from them. Something which at first is novel and new – the big house or the pay rise – gets our attention but we soon get used to it and our attention quickly finds something new to direct itself at.
We do not adapt to everything, however. The unpredictable nature of the commute continues to grab our attention as we get held up in traffic or as our train arrives late. The unpredictable noise of the traffic also continues to grab our attention: a study of first-year college students in the US, for example, found that annoyance with noise in college increased over time. Moreover, there was increasing pessimism about adaptation to highway noise: after four months, under one-third spontaneously mentioned noise as something they disliked in the neighbourhood, whereas over one half did after 16 months. The problem is that we are not very good at predicting what we will adapt to and what will continue to grab our attention.
Our memories do not serve us very well either. Imagine you have just got back from holiday and are asked how much you enjoyed yourself, and whether you would go back again. If you are anything like other people who have been asked these questions, then your answers will be explained by two things: the most extreme and the most recent feelings. This is known as ‘the peak-end effect’. Your feelings at other times during the holiday would hardly matter at all. Your overall assessment of the holiday would also completely disregard how long it lasted: ‘duration neglect’. Our memories, even the most recent ones, are etched with extremity and ‘recency’ but not with duration. As such, they are imperfect guides to our past experiences – but they do drive our future behaviour.
For the best part of 100 years, economists have defined ‘utility’ (well-being) according to the degree to which our preferences are satisfied. If we assume that individuals are rational, fully informed and seek to maximise their utility, then the choices they make will be, by definition, those that maximise expected utility. But prior to this, we thought about utility in terms of feelings. The two definitions would produce similar results and recommendations only if people wanted most what they will eventually enjoy best. But they do not. A good example of this is that smokers appear to become happier after cigarette taxes increase.
We are all in favour of people being free to choose but our choices – and our memories of the experiences that follow them – may not be a very good guide to our well-being.
Given all of this, we focus on the original conception of utility in economics – happiness – rather than the current focus on the degree to which preferences are satisfied. The important thing about happiness data is that they allow us to say what is important in people’s lives when they are not thinking about how important those things are: when they are not engaged in lay rationalism, for example. We cannot overstate how important that is. We ask about happiness and then we find out lots of other things about people, including their income, health, marital status, housing and neighbourhood. Then we look at how important these other things are in explaining people’s happiness. In this way we can find out how important neighbourhoods etc. are, without directly asking people how much they matter, thereby bypassing their ‘lay rationalism’.
In relation to urban factors, there is some evidence to suggest that air pollution and noise pollution can affect happiness. It has also been found that living in a highly populated urban area, compared to either a smaller urban town or a rural village, is negatively associated with happiness. There has, however, been little causal work examining how the physical appearance and construction of the neighbourhood affects experiences.
The measurement of happiness is gaining increasing interest from the academic community and policy-makers around the world. The UK government recently took our recommendations on how to measure happiness in large surveys, and in 2012 we will have over 200,000 British respondents to a range of happiness questions. This will mean that we have the breadth of coverage in the UK to examine clear regional and locational differences in people’s happiness. This could then be correlated with quality of life indices that are often used in regional research, mostly in the US, in order to explore the relationships between happiness and quality of life measures at a regional level.
The OECD (Organisation for Economic Cooperation and Development) is also very interested in monitoring happiness in large samples and these data will allow researchers and policy-makers to look more closely at international differences in the determinants of happiness. From the Gallup World Poll it seems that many of the things that matter to happiness are quite similar across different countries and cultures around the world. Things could be different for urbanisation of course, but we have so far not had the data available to explore this. In future work, we should seek to look at how urbanisation affects happiness in one country compared to the next and to look further for some of the contributory factors to any observed differences.
It is fair to say that much of the evidence to date is based on associations: we know that commuting is associated with lower happiness, but we cannot say for sure how much of that lower happiness is directly caused by commuting. This makes it pretty hard to say just how happy commuters used to long commutes would be with shorter commuting times. We have yet to fully establish causality and control properly for selection effects. In order to do so, we should continue to analyse secondary data, especially household panel data, such as the UK Household Living Survey and the German Socio-Economic Panel. We could also start to examine those who move house, their reasons for moving and the implications for their happiness. What is potentially interesting about the UK data is that it includes children as part of a youth survey. As children have little say in where they grow up, we can find out how the characteristics and experiences of the local neighbourhood when they are young impact on their circumstances and happiness when they are adults. This will help us to start to map the life-course consequences of cities and localities on people’s outcomes and happiness.
The lack of suitable data does limit how far this approach can take us, however. We should therefore also make much more use of field experiments, which, with innovative designs and the right research partners, have the potential to shed some significant light on the causes of happiness that are of interest to urban economists and others. For example, akin to the US ‘Moving to Opportunity’ scheme, we could randomly give housing vouchers to individuals living in poor neighbourhoods to enable them to live in better neighbourhoods. We could assess their happiness and other characteristics before giving out the vouchers, and then follow up with all the residents once they have moved. Such a study with a clear strategy for identifying causality will allow us to determine the impact that neighbourhood and cities actually have on people’s lives and experiences.In taking this and other research forwards, we recognise that the traditional economic approach to urban issues is important in understanding why we gather and live together to form cities. We have a clear incentive to do so since we are not self-sufficient. So by gathering in cities, we can produce more goods (due to economies of scale and comparative advantage) and therefore demand more goods, and have higher economic resources as a result.
We do not always optimise our economic resources, let alone our happiness. This has important implications for how we structure and plan major urban areas, and how we judge whether planners actually increase people’s happiness. Most people around the world already live in urban areas, generating major problems to overcome through urban planning and other interventions, especially with respect to environmental and health challenges. At the very least, we should incorporate happiness effects into our models of cost-benefit analysis and indeed the UK is already making moves in this direction.
The planning and design of urban spaces and cities can greatly enhance people’s lives. They can also make people miserable. Researchers in urban economics, as elsewhere, need to get out into the field and directly assess the well-being and behavioural consequences of different places and spaces.

Paul Dolan is Professor in the Department of Social Policy at the LSE.

Robert Metcalfe is a Research Fellow in Economics at Merton College, University of Oxford, where he conducts field experiments in energy consumption, charitable giving, non-market valuation, resource use and savings.