Measuring poverty: don’t miss the point

Jess Berentson-Shaw from the Morgan Foundation urges the new government to not just measure poverty, but measure improved lives

Great news. Whoever is in government next is going to start formally measuring child poverty, or more accurately the number of families with children in poverty (most children do not exist as separate autonomous units from their whānau).

While you may have thought getting people in government to agree to measuring poverty was hard work, just wait till they start arguing over which measures to use*.

However, in all this talk about measuring poverty, we risk losing sight of the actual point of it - improving families’ lives. In fact while we focus on the pros and cons of some pretty ho hum metrics of poverty, people in government risk becoming oblivious to more innovative ways of combining poverty measurement with improving outcomes. In doing so they put us in danger of relying on expensive but ineffective policies. 

Let’s start by talking about the standard poverty measures.

Poverty measurement discussion: like last week’s tip top loaf

We have had many conversations lately about the pros and cons of income poverty and material deprivation measures - the two main types of measures used. The income measures are all about money coming into a household, while material measures are focused on the things people lack, for example, food quality, clothing, healthcare, transport access, a warm, dry home, or the ability to pay an unexpected bill. There are other measures relating to how bad poverty is (severity) and also how long it has gone on (persistence).

Researchers agree (though I not sure about politicians) that a “kete of measures” is best given the problems in all of them (here is a summary). In the table below I have outlined most of the poverty measures we use in New Zealand and internationally.  

Main Measures of ‘Poverty’ Used in New Zealand

I am certainly not arguing that we don’t use these measures, rather that we should use them as a given but move on to a much more exciting and innovative ways of thinking about measuring deprivation and linking it to meaningful outcomes.

Vanity statistics** and why they can hamstring in policy

If you spend too much time looking at the speedo on the car, trying to stay safe within the speed limit, next thing you know you have run off the road and taken out someone’s letterbox. Likewise, if people in government spend too much time focused on a particular poverty measure, for example moving people over the 60 percent median income line, because it makes them feel good (aka vanity statistics), they can become oblivious to the real goal - improving outcomes for children and families who live in poverty.

Poverty is a risk factor; it is not the ultimate outcome. We care about poverty because it prevents people from having, and making most of opportunities to live the kind of lives they value (which ultimately is not great for any country). The solution is to think about measuring poverty in the context of improved outcomes and we can now do that.

Thinking about poverty measurement in the era of big data

In the last few years things have got a whole lot more exciting in the world of measurement. In New Zealand we have joined together some very useful government databases into one big database. We have joined up Statistics New Zealand data, including the census, with IRD data, justice data, health data, education data (what there is of it), and called it the integrated data infrastructure (IDI). The IDI is a beautiful thing if used ethically and appropriately.

One group has done just this and developed a new way to look at poverty in New Zealand. The team, led by Dr Daniel Exeter at the School of Population Health in Auckland, have created the Index of Multiple Deprivation (IMD). It is a tool that allows us to look at different types of deprivation in small neighbourhood areas. Follow the link to the online maps and have a look at your own neighbourhood.

There are three really exciting things about this new tool. First, it incorporates different types of deprivation in one tool – for example employment, income, crime, housing, health, education, and access to services. And we can explore each type independently e.g. housing deprivation versus employment versus income. While we have used multiple measures of deprivation in one tool before (in the New Zealand Deprivation Index), we have never been able to explore each type - this is a very powerful evolution in poverty measurement, given that research tells us not all types of deprivation will be an issue for all people, and some people face multiple forms of deprivation.

Improving lives through poverty reduction does not require we ‘zero in’ on each person.

Second, the tool has been designed to ensure that individual people cannot be identified from the data, while still enabling meaningful analysis of the issues (the neighbourhood areas that can be looked at comprise on average about 712 New Zealanders each). In the past in order to maintain anonymity in these types of measures, using census data for example, researchers would either have to ignore a lot of data (called suppression) or put quite large groups of diverse people together (both reduce the usefulness of the analysis). In a country the size of New Zealand, with a population that can be quite spread out, there is a risk of being able to identify a person just by the nature of their circumstances, and it is important not to do that (I discuss why in a bit).

The third exciting thing about the tool is it will allow researchers to link the different types of deprivation measures to meaningful outcomes for children and families (data that is also collected in the IDI). So for example it would be possible to look at the relationship between housing deprivation and admission to hospital for children under five, better still we could track that over time and see whether we can improve that particular outcome as we reduce housing deprivation, or whether income deprivation has a greater impact, or even whether it is only doing both together that works. You begin to see how this could have real value in terms of moving beyond just reporting on a vanity statistic to improving an outcome through reducing poverty.

By using such a measure, people in government with the help of researchers could develop a very meaningful framework for how to improve outcomes for families and children living in poverty and what change we would expect to see over time.

Data privacy matters

People may wonder why researchers do not want to ‘see’ individuals in poverty measurement. The first reason is privacy. The most ethical way to use people’s personal and private data is not to look at individuals where you can possibly help it. It maintains an important level of trust and the social licence of people’s data collection by governments. Don’t look at personal data if you don’t need to, which leads to the second reason - we don’t need to ‘see’ individuals to do something about poverty and poor outcomes.

Improving lives through poverty reduction does not require we ‘zero in’ on each person.

While the impact of deprivation is on individuals, it does not then follow that those individual tailored solutions are where we should focus. Rather, what scientists know from years of research is that it is the conditions that we live our lives in that have the greatest impact on our choices and decisions in life. When I say ‘the conditions of our life’ I mean housing quality, access to healthcare, the security of employment, the quality of our education, our level of income, the systematic bias acting for or against us etc.

If the conditions of our lives leading to poor outcomes don’t change, then making alternative choices or changing our behaviour is terribly difficult. Think about trying to get more sleep while also subject to the sleep patterns of a baby. No matter how much sleep advice you got, the conditions of your life would make it very hard for you to improve your sleep. However, if someone else was to take care of the baby at night, that would give you more choices and make your individual efforts that much more effective.

So what we know is that we don’t actually need to see every individual in the country to see the impact (good or bad) of these conditions on the New Zealand population. Yes we need relatively small groups of people, but not individuals, to both measure the problem and any improvements. And that is great news for data privacy and changing lives too. That’s why so many studies around the world promote interventions that aim to improve particular neighbourhoods and conditions, not people.

So in summary, static poverty metrics risk people in government setting policy that just gets some families over a line in the sand and saying “job done”. The vision here is to ensure all families – especially those with children – have real opportunities to thrive, and to achieve this we need our measurement of the problem to encompass the vision.

Dr Jess Berentson-Shaw has written a book on the best solutions to family poverty in New Zealand, called Pennies from Heaven.

* Note that New Zealand is a signatory to the Sustainable Development Goals, which requires that we measure and report on poverty in New Zealand to an international body, the goal being to halve “poverty rates” by 2030.

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