Monica Alexander maps Canada’s hidden childcare economy through data science

The Trudeau government’s 2021 announcement of a $10-dollar-a-day childcare plan was welcomed by many Canadian families. But barriers affecting the plan’s full implementation have meant that childcare continues to be a major concern for parents working outside the home.

Today, many of those seeking childcare resort to the unregulated “grey market” of nannies and babysitters: a labour sector that, up until now, has not been studied in depth.

“Childcare in Canada is still pretty fragmented, in particular for parents with kids of preschool age,” says Monica Alexander, an associate professor in the Department of Statistical Sciences, cross appointed to the Department of Sociology.

“One problem is that we don’t often have very good data on childcare providers. A lot of this work is done part-time, so workers don’t show up in tax file records. And this information isn’t generally captured in census data or large-scale population surveys, because it’s not often someone’s main profession.”

Alexander is now designing statistical methods to learn more about the world of nannies and babysitters, in an effort to identify the factors that influence their pay and working conditions. Better data, she contends, could lead to policies that ensure higher quality childcare, as well as better protections for those working in the field.

The project is supported by the Department of Sociology’s Centre for Global Social Policy, whose researchers are engaged in a variety of social policy projects aimed at improving societal welfare.

So far, Alexander and her team have spent roughly a year and a half on the childcare research project; they are now in the process of securing funding to expand their findings beyond the Greater Toronto Area (GTA) to the rest of the country.

One recent area of focus is online platforms, which can offer valuable insights into groups that are often overlooked in official data. For this particular project, Alexander has been analyzing publicly available profiles on a widely used website that connects nannies with families seeking childcare.

She discovered the site several years ago when, as a mother of two young children, she needed a caregiver. “This site is very commonly used. When this project came up, it was the first thing I thought about,” she says.

These profiles provided her team with a rich source of information about caregivers’ backgrounds, skills, and availability — data that could be organized and analyzed to reveal trends in the sector.

Alexander combed through some 15,000 unique profiles. Data from about 6,000 in the GTA alone provided the basis for her project’s recent working paper, entitled Unregulated, Unknown and Highly Necessary: Grey Market Nannies.

The research revealed some interesting findings.

“On the site, people talk about themselves based on how much they are willing to be paid,” Alexander says. “So people who wanted to be paid a higher hourly wage were much more professional: they’ll point out that they have certain qualifications, and would like to develop skills in your child. And often these people were looking for part-time work.

“On the other hand, the language of people who were asking for a lower wage was much more emotive and care-based: they talked about providing a caring, loving environment for your child. These people were also more likely to be looking for full-time work.”

The team also found that a nanny’s average salary was $30, while the median was $20. Many of those advertising on the site were from Canada and the Philippines, but there were also quite a few from France.

Further, “the age range is really, really narrow,” says Alexander. “Most people are between 20 and 30; you hardly ever see anyone older. Of course, younger people are using a web-based platform and that’s what we’re picking up on. But it’s still valuable data.”

Alexander sees this step as just the beginning of what she hopes will be a longer project.

“Data science can give us a broad picture. But it’s most powerful in combination with qualitative research as well — sitting down and having conversations with people.”

This project fits in with Alexander’s focus on developing statistical methods to measure disparities in demographic and health outcomes. In the past, she has worked on other initiatives with organizations such as UNICEF, the World Health Organization and the Gates Foundation.

“A central thread of what I’m interested in is trying to build up evidence for population groups where we don’t have very good data,” she says. “From a policy perspective, it’s really hard to change policies to try and help disadvantaged populations if we don’t have data on them. And it’s often the case that we have the least data on the people who need the most help.”