The inevitable inequity of unpaid internships

A few years ago, the story of a UN intern from New Zealand living in a tent by Lake Geneva made international headlines. Apparently Geneva residents, along with the rest of the world, were “shocked that the famous and much-loved institution should be connected to such a case.”

The only thing that shocked me was that so many were unaware of this ugly reality that is a persistent infection of the international development industry.

I have extremely strong opinions about unpaid internships. Part of this may stem from my generation’s collective rage toward the economic disaster into which we were dumped after finishing university, and our resulting economic desperation. Unpaid internships are certainly not unique to global health or international development, and the Great Recession left us particularly vulnerable to them.

Most of my frustration, however, comes from the fact that this trend is particularly strong in global health – a field which is ostensibly focused on building up health systems to support the poorest and most vulnerable. I discovered that, despite being the child of a first-generation immigrant with fluency in both Portuguese and French (on top of an MPH), my financial inability to work in unpaid positions (read: I don’t have rich parents) turned out to be a permanent barrier to entering a field that I was so passionate about. Dozens of applications went unanswered over the years even as my resume accumulated increasingly advanced public health jobs in the U.S. The only explanation I could think of was the catch-22 that plagues the industry. You can’t get jobs doing development work unless you already have closely related experience doing development work – which means that the first few times are unpaid. Multiple well-known development professionals have confirmed this, and most appear to have just accepted it as an unfortunate reality. My experience is not unique.

This irony of using unpaid interns to drive the entry-level work of global health is finally beginning to creep into the peer-reviewed literature. As an editorial in last month’s Lancet Global Health pointed out about WHO’s internship program:

[WHO’s] mandate, to promote the health of people worldwide, requires it to build technical and operational skills within the health systems of its 193 member states. For many of these states, particularly those of low income that face growing disease burdens, developing skills in the next generation of public health professionals is imperative.
WHO’s Internship Programme exists to support this goal. …However, less than 20% of interns come from developing countries. This imbalance in member state participation has two principal causes: an absence of financial support for interns, which precludes the participation of many from low-income and middle-income countries; and an ad-hoc recruitment process that favours candidates with connections in well-established academic institutions, typically in high-income countries. The result is a missed opportunity for WHO and inadvertently undermines its own objectives on human resources for health.

Oh, unpaid internships restrict the pipeline of global health professionals to rich people from rich countries? Shocker.

Many aspiring global health professionals (including myself) have groused about this reality, swapping anecdotes of spreadsheets of rejected applications and job boards glutted with positions requiring at least a decade of experience. But ground-level conversations between those of us on the outside looking in don’t move the needle. To have any chance of addressing the problem, the first step is establishing that it exists across the industry – and an excellent way to do that is with data.

The Global Health Jobs Analysis Project was born out of a pair of conversations I had at the 2015 Annual Meeting in Chicago with IH Section members who shared my frustrations. After exchanging similar stories of scouring hundreds of job vacancies for non-expert positions, to no avail, we resolved to put together a team to collect and analyze data on a job market that most global health MPH grads simply cannot crack. Two years, a thousand job vacancy descriptions, and six months of peer review later, our analysis was published in the open-access journal BMC Public Health. From the abstract:

We analyzed the data from 1007 global health job vacancies from 127 employers. Among private and non-profit sector vacancies, 40% (n = 354) were for technical or subject matter experts, 20% (n = 177) for program directors, and 16% (n = 139) for managers, compared to 9.8% (n = 87) for entry-level and 13.6% (n = 120) for mid-level positions.
Our analysis shows a demand for candidates with several years of experience with global health programs, particularly program managers/directors and technical experts, with very few entry-level positions accessible to recent graduates of global health training programs. It is unlikely that global health training programs equip graduates to be competitive for the majority of positions that are currently available in this field.

Our analysis is related to the unpaid internship problem because it shines a light on the “top-heavy” nature of the global health employment field. In a typical industry or discipline, you would expect to find the largest number of positions at the entry level, with increasingly fewer mid-level, managerial, and technical expert or director positions. Our data – which only included paid positions – showed the exact opposite. There were more director-level positions than managerial spots, and nearly half of the positions were for technical experts. This certainly lends weight to the Lancet Global Health editorial’s suggestion that the vast majority of the initial work needed for “developing skills in the next generation of public health professionals” is unpaid. This assumption even appeared in our peer review, when one of our reviewers asked why we didn’t include internships in the analysis:

Why not include unpaid internships in the study? Aren’t these ‘entry-level’ in a way? Knowing about the prevalence of internship jobs would help better characterize the potential mismatch between graduate programs and job markets.

Our response:

We deliberately excluded unpaid positions because they are not available to all
applicants in the U.S. global health employment market. While they may technically be entry-level positions, they do not provide candidates with the means to support themselves or their families. […] Such positions are effectively restricted to applicants with a working spouse, affluent families, and/or independent wealth.

There is something perverse about an industry that restricts careers doing meaningful work helping the poor to a small handful of extremely wealthy people, no matter how well-intentioned. The end result is that program beneficiaries cannot enter the industry and thus end up having no say in how those programs are designed, administered, or evaluated. Equally important is the consideration that an industry overwhelmingly staffed by people with the same backgrounds will inevitably suffer from the lack of diverse experiences and perspectives. Again, global health is not the only field that suffers from this cancer, but the stakes in this line of work are incredibly high. A WaPo editorial on the same phenomenon on Capitol Hill raises these very questions:

What consequences arise when Congress effectively restricts its entry-level workforce to those willing to take on debt via credit cards or those for whom money is no object? It almost certainly makes it more difficult for the child of a teacher [to pursue] the ultimate public service career.

If the only way to thrive in Washington is by way of someone else’s bankroll, how can those entrusted to find policy solutions to this country’s problems come from anything lower than the upper middle class?

How indeed.


The Promise of Data for Transforming Global Health

I recently came back from a field visit and as my organization’s designated data person (among the many other hats I wear), I think constantly about the role of data in our work and more broadly, its role in global health.

We’ve always had a problem with data in our field, more specifically the dire lack thereof. Recent efforts to spotlight the lack of high quality data in global health has led to somewhat of a data renaissance. And you know it’s a big deal when Bill Gates throws his weight behind it. It seems like every global health innovation talk I go to nowadays portrays data (in all its forms, from big data, predictive analytics, and machine learning) as the ultimate game changer in global health. Data is so much easier to collect now with the various technologies and innovations available. Its potential is pretty obvious and I don’t disagree that data can and will create more positive changes in global health. But every time I attend one of these talks or I get an email alert about another new data innovation challenge, part of me gets really excited and the other part remains skeptical.

Anyone who has tried to implement a data collection initiative in the field, whether for research, monitoring and evaluation, or donor reporting, knows the many challenges faced when working in already resource-limited clinics and hospitals: the questionnaires are long and time consuming, we don’t have the resources to hire people to do just data collection (which is especially true in smaller facilities), data collection activities take away from clinical activities, data quality is poor, the staff spends a whole week every month doing reporting, every donor wants a report on different indicators, no one at the clinic knows how or has the time to analyze the data, the data is not in a format that is easy to use, etc. And the list goes on.

One huge barrier to accurate data collection involves the inordinate amount of burden placed on health care providers and/or clinic staff to collect and report data. Data collection is often a task that already busy doctors and nurses have to undertake in addition to their clinic duties. Hiring an extra data collection person is one solution, but may not always be sustainable outside of a research study setting. Reporting data to donors is not any less painful. It is too often a rote and uncoordinated endeavor. Donors ask for the same data, but sliced and diced in a slightly different way. Those asking for data haven’t exactly done a good job making data collection easy to do. Shorter questionnaires, standardizing indicators, simplifying and coordinating reporting are different approaches for addressing these issues. Getting providers and clinic staff to collect high quality data though is another beast. Some argue that doing regular data audits will fix the data quality problem. Others argue that mobile data collection has reduced data entry errors. Mobile data collection has certainly made it easier to collect data and scale-up data collection activities.

And while a lot of work is being undertaken by major development agencies and smaller NGOs alike to improve their data collection efforts in order to deliver on the promise that data has to offer, I’m not entirely convinced we’re there yet. A huge part of my skepticism in why data hasn’t yet reached its transformative power in global health is because even though I think we’ve spent lots of resources in building capacity to collect data, we haven’t spent equal amounts of efforts building capacity for local team members to use the data in a meaningful way.

If those who collect the data don’t understand why or how the indicators they collect impact patient care, then why do it? Although national level data is helpful in understanding what the different health needs are and how to allocate resources to address them, the interventions needed to dramatically move the needle when it comes to decreasing morbidity and mortality happen at the individual facility level, outside of the research setting. The frontline healthcare workers that help in the collection and reporting of data very rarely get the data back in a way that can help them understand how to improve care delivery and health outcomes for their patients.

I believe in the potential of data to transform global health but there are many obstacles to overcome before this happens. First things first, instead of thinking about data collection as an activity that providers and clinic staff have to do, it should be an activity they want to do. By having data available to providers that is easy to understand, timely, and meaningful, only then can the promise that data holds for transforming global health be fulfilled.

#D4CA Challenge: UN Global Pulse calls for research proposals to analyze business data to combat #climatechange

Note: This was cross-posted to my own blog.

Rose Schneider, chair of the IH Section’s Climate Change & Health Working Group, shared this information about the Data for Climate Action challenge. It’s an initiative by the UN’s Global Pulse to recruit researchers and data scientists to “leverage private big data to identify revolutionary new approaches to climate mitigation and adaptation” – that is, use corporate datasets, which have been de-identified and made available by participating companies, for projects or analyses that “generate innovative climate solutions.” According to the press release:

Data for Climate Action will target three areas relevant to the United Nation’s Sustainable Development Goal on climate action (SDG 13): climate mitigation, climate adaptation, and the linkages between climate change and the broader 2030 Agenda.

The challenge aims to generate original research papers and tools that demonstrate how data-driven innovation can inform on-the-ground solutions and transform efforts to fight climate change. It builds upon the model of data science competitions pioneered by organizations like Kaggle, and company-specific initiatives to share big data for the public good, such as the “Data for Development” challenges hosted by Orange.

Researchers who are selected to participate in Data for Climate Action will have four months to conduct their research. A diverse panel of experts in climate change and data science will evaluate final submissions based on their methodology, relevance, and potential impact. Winners will be announced in November of 2017.

The data being offered includes retail transaction data, social media posts, meteorological and air quality data, and user-generated data on road conditions. Data sets can be combined with each other or with other publicly available datasets like those featured on Data is Plural. Individuals or teams can submit proposals, and the only apparent requirement is that all participants be at least 18 years old.

They’ve apparently extended the deadline from April 10th to the 17th, so any analysts or programmers who aspire to code for the public good still have ten days to get their applications together and apply.