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Now on to the podcast!

In this week's Conversations with Data episode, we spoke with Professor Deborah Stone, a renowned policy expert and political scientist at Brandeis University. She speaks to us about her latest book, "Counting: How We Use Numbers to Decide What Matters". She explains how policymaking is shaped by the worship of numbers and why journalists and policymakers should be sceptical of "quantitative imperialism".

You can listen to the entire podcast on Spotify, SoundCloud, Apple Podcasts or Google Podcasts. Alternatively, read the edited Q&A with Professor Deborah Stone below.

What we asked

Tell us about your career path. How did you first become interested in social science?

When I started out, I really grooved on math and science. Math and science came easily to me, unlike literature, history and social studies. Besides, I was searching for the meaning of life. In biology, I looked through a microscope and I was just enchanted that there was so much pattern in the world. I thought the answers were to be found in science and math.

When I got to college, I majored in Biology. But my friends told me that the best professor was the teacher who taught Introduction to Government. I had no interest in politics, but I took it. And it turned out the whole first semester was Political Theory. We started with Plato and we marched on up through all the political theorists. I thought "This is wonderful. This is a subject that's about how to make justice in the world." That seemed like the most noble aspiration you can imagine.

I went to graduate school in political science. As I came out of graduate school, a new field called public policy emerged: the study of what government does to make life better for people, to solve problems and improve people's well-being. It combined the philosophical quest for justice and goodness with the practical "how do we accomplish it?" So that is how I came to do what I do.

In the 1960s, quantitative analysis became central to Political Science. How did that sway you to write this book?

I had my first run-in with what I call "quantitative imperialism" when I took my undergraduate Introduction to Economics class. I remember the professor put up a whole bunch of supply and demand curves on the blackboard and talked to us about how people decide what to buy or how much to buy according to the price, and that everybody works to maximise their self-interest and get the most for the least. I raised my hand and said, "I don't think price is the only thing people think about. I think that's really an oversimplification." And the professor said, "Yes, I agree with you that it's an oversimplification, but if we strip things down and we make this simplification, it can lead to some very powerful predictions."

Something rubbed me the wrong way about the idea that you could ignore things that are really important to people and then say that you're making powerful predictions. As the decades progressed, we went to what people are now calling "market fundamentalism". We've organised entire societies and political economies on the idea that people pursue their self-interest and care only about maximising either profit or personal gain.

What did you hope to achieve by writing this book?

It's a bit grandiose, but I wanted to launch a counter-revolution against quantitative imperialism. I think there's so much worship of numbers, especially in public policy. Government calls for "evidence-based" policymaking by which they mean numbers. Journalists call for facts by which they mean numbers. At universities, most professors tell students, if you don't do statistics, you won't get a job or you're not doing real science.

A lot of mathematicians and economists, even ones who are very critical of our use of numbers in a good way, claim numbers are the most powerful instrument we have to understand reality. If they are objective and if those numbers really can tell us the facts and the real patterns in the world. I don't think that you can get much understanding of human character by counting things. I don't think you can get much understanding of human relationships by counting. Sure, we have algorithms that can fix people up on blind dates and a lot of them work out into very happy marriages. But algorithms can't fix marital problems.

Who is the audience you intended to reach?

The book aims to encourage a little bit more scepticism about all these claims. I think that you'll be a better data journalist and a better data scientist if you know where your data come from, meaning the raw numbers, how things get counted. The book is really for everyone interested in public affairs. Everyone who enjoys reading the news and wants to understand it and make sense of information about how we live our lives. I want no one to be intimidated by fancy statistics.

In the book, you examine early childhood development and how we learn to count. Why is that important?

That's really the key to my book. I want people to understand the mental process behind counting. I'm not talking about PhD brains here. I'm talking about pre-school, because we all learn to count usually before we go to kindergarten. I often ask people if they can remember learning to count. I haven't found anyone who remembers learning how to count. Some people remember learning how to read, but no one remembers learning how to count or learning how to talk. It's something our parents teach us and the adults around us teach us, but we just absorb it somehow.

When kids learn to count and when they learn to talk, they're really learning how adults categorise things. They're learning what fits under the word cookie, for example, and what things should be counted, therefore, as a cookie. This is why we can't talk or think about things without categorising them. So I would argue that categorising is the most powerful tool we have for understanding the world, not counting, because categorisation comes before counting.

In the book, you talk about how numbers have the aura of power. Tell us more.

I'm a political scientist, so I see power everywhere. I think that's maybe how I look at numbers a little bit differently from economists and mathematicians, because I'm looking for how numbers exert power. Hannah Fry says numbers are a source of comfort. And I think in the COVID-19 pandemic that has been true. They offer an illusion of certainty. They offer us hope of control and certainty in numbers.

Another way numbers exert power is that they include and exclude people. So all these scoring systems for job hiring, for giving insurance, bank loans, giving pay raises and promotions -- all of those boost some people up and leave some people out. Numbers can be used to draw lines and include and exclude people.

When it comes to race, how would you redesign the categories in the U.S. Census if you could?

This is a giant conundrum and I don't have a good answer for it. I've thought about it a lot. I've read everything I could find about the census -- but here's my dilemma. Race is the quintessential thing that can't be counted. It's an idea that some people have about other people. It's a social construct. It's not a thing. And there's a simple reason why we can't count people's race: sex. Even if you assign a race to two people, what do you do when those two people have kids? How do you count their kid?

In the United States, we have this sickening tradition of the one-drop rule that was applied to people who at the time were called Negroes. If they had one drop of Negro blood, they would be classified as Negro and therefore banned from all things that whites could do. But all people are mixtures of backgrounds themselves and then they produce kids. A mathematical formula for race makes no sense.

One of the striking things I found out in researching this book is how predictions are made about how in the year 2042, the United States will no longer have a white majority. It will be "minority-majority". All those predictions are based on a little categorising decision that the U.S. Census Bureau does when it makes projections. It takes the children of people who are in "mixed-race marriages" and counts them as the same race as the non-white parent. So the more racial integration we have -- and what could be a better sign of racial harmony and integration than intermarriage -- the more the proportion of minorities increases. This way of categorising who counts as a minority, therefore, sends out a frightening message to whites. This is a thing that is driving white supremacists crazy. That is the real danger of counting race. I think it can't be done. There's no correct way to do it given human reproduction, so I am not sure there's a good way to do it.

But here is the other side of my dilemma. In the United States, we have counted race since the beginning and we have made laws and policies and continue to do that on the basis of race. The effects of race-based policymaking and decision making are playing out and will play out forever, into the future. That's why we have so much wealth inequality between the races in the United States -- because blacks weren't allowed to own homes and they weren't given mortgages. I think that the dilemma is how can you begin to remedy these inequities unless you take account of race.

Finally, what should data journalists be mindful of in their data storytelling?

Don't take the numbers for granted. Ask three questions about the numbers. What was counted and what was not counted? Who counted? And why did they count? I think those are the same questions you'd ask as a journalist: what happened, who did it and why.

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Our next conversation

Our next Conversations with Data podcast will feature Jan Diehm. As a senior journalist-engineer at The Pudding, she will speak to us about her creative process for designing compelling visual data essays -- most notably her famed piece with Amber Thomas examining the size of women's jeans pockets. She will also tell us how to pitch a story to The Pudding.

As always, don’t forget to let us know what you’d like us to feature in our future editions. You can also read all of our past editions here. You can also subscribe to this newsletter here.

Onwards!

Tara from the EJC Data team,

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