Uncovering systemic inequality with data

Conversations with Data: #87

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Welcome to the latest Conversations with Data newsletter!

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Now on to the podcast!

Using data to investigate systemic inequalities is a powerful way for journalists to tell stories with an impact. In this week's issue, we caught up with Sinduja Rangarajan, Bloomberg's senior investigative data reporter, to discuss her experience in covering a range of topics, including immigration issues in the United States. She talks to us about the importance of community and the power of bringing your lived experience to work.

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

What we asked

Tell us about your career path into data journalism. How did that come about?

When I decided to be a journalist, I already had some background in data in another profession. But my primary reason for being a journalist was to write about systemic inequities and to write stories that mattered the most, particularly thinking about the idea of community. Data was just a way to tell those stories in a more powerful way. I had these skills already, which made it easy for me to pivot.

I started as a data reporter at Reveal and did data journalism at Mother Jones. I have approached my reporting to uncover systemic inequities using data and grounding it with human stories. I've done investigations around disparities in the tech workforce. I've also written stories about immigration and inequities that resurfaced during the pandemic. Most of it is driven by data, but it has a lot of narrative components to it as well.

Let's hone in on your immigration reporting. You covered a story looking at the rejection of H-1B visas for highly skilled immigrants in the United States during the Trump administration. Tell us about how that story came about.

Journalism is such a community-driven profession. Fundamentally it's about telling stories about what's happening around you. In my case, it just happened that I'm an immigrant. My husband's an immigrant here in the United States, and he is on an H-1B visa, a short term visa for highly skilled immigrants. The H-1B visa is a painful and annoying application process. You have to renew those visas, and they don't come with much long term stability. But at the same time, the renewal would happen.

Many of our family and friends were also on H-1B visas, and I was seeing things and hearing things that I'd never heard before. People who had stayed forever in the United States on those visas and held the same job were packing up their bags within three days because their visas wouldn't get renewed. This came completely out of the blue for them. I began looking into this more thoroughly.

What did your investigation reveal?

Former President Trump had enacted a lot of policies using memos. He and his political advisor Stephen Miller started cracking down on H-1B visas. Their political reasoning was that these people were taking away American jobs, and they were not really high skilled. But my investigation found that they were turning away a lot of really highly qualified candidates on H-1B visas. These were people with PhDs from Stanford University or master's degrees from elite universities.

I determined that by building my own database. I knew that a lot of people were filing lawsuits. Many were asking for appeals to the United States Citizenship and Immigration Services Appeals Board when their applications were getting denied. By looking at those appeals and lawsuits, I got a sense of who was getting rejected and how many lawsuits were being filed. Historically, that was at a high. The data also told me that people were getting denied, and they would go and appeal to that board, and their own internal board would reverse the decision at a historic rate. And if you filed a lawsuit, the judge would say they have a legitimate case.

In most cases, they would get the approval. This all pointed to the fact that these memos and the rules that Trump had created were misapplying the law. They were designed to track and crack down on immigrants. He was obviously doing it on the border, but he was also doing it with high skilled immigrants -- and this was the missing piece of the jigsaw puzzle my story focused on.

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You also wrote another story for Mother Jones about three Afghan families who managed to win the diversity green card visa lottery to the United States as American troops were pulling out of Afghanistan? How did that story come about?

I knew about the closure of American consulates and visas overseas due to the pandemic. As a result, many different types of visas were delayed or not processed. When President Biden announced pulling forces from Afghanistan, and news broke over the Taliban taking over, I began to wonder what this meant for the people who won the green card lottery visa in Afghanistan. Then I realised that these people from Afghanistan who won the lottery weren't going to come to the United States. They were in an extremely difficult situation, especially because of the news the Taliban were taking over.

The rest of it was finding the source. I reached out to a few of my past sources. One attorney put me in touch with a few Afghani families. I spoke with them through a translator, and I wrote the story. A lot of this immigration data is public. At that point, it was just a matter of digging and finding the relevant numbers and context for the story. Next, I pulled those numbers from the U.S. State Department. I also worked with an attorney filing a lawsuit who knew the numbers.

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How does this lived experience help your reporting?

Bringing your own personal background to work can be really helpful. I'm not an immigration reporter. That's not my beat, even. However, I uncovered this story because of what was happening in the community. I noticed most reporters in the country didn't pay attention to this issue because there was so much going on with immigration at the time. There are some terrific immigration reporters in the United States. I've noticed that some of the people who do the best immigration work understand that particular community well and have some connection with that community.

An example that comes to mind is Aura Bogado at Reveal, a former colleague of mine. The empathy and the humanity that she brings to her stories means she can challenge many supposedly well-accepted myths about certain people and then point out that that's not true. I think it's very similar to the way I approached the H-1B visas and another story I did on H-1B doctors during the pandemic. Those kinds of stories come about because nobody needs to explain the context or background to you. You are already connected to that community. That means you can understand their position when they make an argument. You can build trust with your sources very easily because they believe you will be fair to them and their life story.

As a data journalist, what new skills are you most interested in learning next?

I would love to learn Ruby on Rails or Django. I want to skill up with my JavaScript and CSS because those languages have changed significantly. I used to write code in JavaScript and build tiny apps and use CSS. I've built websites as side projects when I was in school, but things have changed so much. It's so much more sophisticated, and therefore, a whole other challenge is just staying on top of things. You have to pick and choose what you want to keep on top of and lean on others for everything else.

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Would you say you spend most of your time working with data analysis?

Yes, absolutely. Data analysis ties very well with investigative reporting. It supports and is the foundation for a project or an investigative story. You analyse the data. You have the findings. You talk about systemic inequity in a particular way. Then you find characters impacted by the systemic inequity that the data has already borne out. Finally, you write the story. Because I've chosen investigative reporting as one of my paths, I've also stuck with like data analysis.

What advice do you have for budding data journalists?

There are different ways to do data journalism and carve out a path for yourself. What's worked for me is leaning into a platform's strengths, especially when you're in the earlier parts of your career. There's so much energy, enthusiasm and little power in the organisation you're working in. I love doing data analysis, but I was pushed to do my own stories to get out of my comfort zone because that's what was needed to get my stories to the finish line. I couldn't wait for another reporter to finish their story and come and work on the data analysis of my story. So then I learnt writing and investigative reporting. At Mother Jones, I got to do personal essays and do different kinds of stories on a deadline. I learnt how to do things quickly.

I think every platform has its strengths, and at every stage in your career, you would probably have to get out of your comfort zone. My advice would be to go with the flow and try to get out of your comfort zone and get as many skills as you can. You can either be a super-specialist, and if life's taking you there, great for you. But if you are in a place that allows you to do different kinds of stories or report and write along with data analysis, then that's a different path as well. And that's the path that I've taken that's working for me.

Latest from DataJournalism.com

Machine learning can help journalists analyse massive datasets and pinpoint misclassifications for their investigative reporting. Contributor Monika Sengul-Jones explains how with a series of in-depth case studies from Buzzfeed News, Grist, ICIJ and more. The piece also includes a visual explainer on machine learning in journalism. Read the full article here.

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Tara from the EJC data team,

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