Blog

Magpi Awards 1st Place: Pact Fights Child Labor in DRC Mines

Joel Selanikio ON 18 November 2013
pact-drc-mining

As promised last week, we want to dig into each of the three winning entries in our 1st annual Magpi Case Study Contest.  We’re starting today with first place winner Amanda Epting, of Pact, whose team is working in DRC to track conflict minerals, and also to learn more about the causes and circumstances of child labor in mines.

Amanda’s team was able to save time, money — and get more data than expected! — by using the FREE, basic version of Magpi.

 

 

 

From her winning entry:

PACTLOGOIn the Democratic Republic of Congo (DRC), Pact leads the field implementation of a due diligence and traceability mechanism to verify, tag, and trace conflict-free minerals in 208 mines. As part of this program, Pact is also working to eliminate the worst forms of child labor in mining. In spring 2013, we conducted research in three rural and remote territories in Central Katanga where our mining sector projects are underway to better understand the root causes of child labor in the mines.

Initial research was conducted through observation, interviews and focus groups, but the data received was incomplete and Pact decided to conduct an additional survey in order to collect more quantitative data and verify anecdotal information previously gathered. Due to time and budgetary constraints as well as the remoteness of the mining sites and limited staff capacity, Pact chose Magpi in order to collect the additional data

Of course, just knowing that Magpi is being used for such great work makes us very happy, but our judges, and the staff of DataDyne, were especially impressed with several aspects of Amanda’s entry (copied in full at the end of this post):

 

Discussion of cost and ease of implementation

From Amanda’s entry:

Within one week, Pact staff in the US, in collaboration with staff in the DRC, had designed the survey, trained the DRC data manager on the methodology, downloaded the survey onto the phones, trained the staff, and had them conduct a pilot survey. Pact did not have to hire local consultants or pay for non-local staff travel most likely saving the organization several thousand dollars in equipment, staff time and other costs.

Other Magpi users will not be surprised to hear that Amanda’s team was able to implement Magpi themselves, without the need for outside consultants or programmers, but it is remarkable even to hear these issues acknowledged in any ICT4D report.  This link, between truly easy and low-cost technology and expansion of real capacity, is one that we at DataDyne have been pushing for a long time. And in this case the exceptionally low cost of using Magpi means that others who read about the great work by Pact can immediately begin to emulate it — no matter how large or small their budget.

 

Reaction of the Pact staff

From Amanda’s entry:

Staff were very excited to use Magpi and to learn how to use a new technology, that they actually carried out three times as many surveys as expected. Additional staffvolunteered to take part in the research and others even utilized their personal mobile phones for data collection. This interest and enthusiasm extended to the interviewees generating a second unanticipated bonus in that the novelty of the data collection media engaged the interest and attention of the interviewees.

Amazing that the software was so easy-to-use and so interesting to the staff that they actually collected more data!   Of course, we would expect that this effect would diminish over time, but it is a terrific rebuttal to those who still worry that local staff will find new technologies intimidating: this shows that useful, well-designed and easy-to-use technology will be embraced rather than rejected.

 

Read more about Pact’s work with child labor and mining in DRC

 


The full text of Amanda Epting’s first-prize-winning entry:

pactdrcmines

In the Democratic Republic of Congo (DRC), Pact leads the field implementation of a due diligence and traceability mechanism to verify, tag, and trace conflict-free minerals in 208 mines. As part of this program, Pact is also working to eliminate the worst forms of child labor in mining. In spring 2013, we conducted research in three rural and remote territories in Central Katanga where our mining sector projects are underway to better understand the root causes of child labor in the mines. Initial research was conducted through observation, interviews and focus groups, but the data received was incomplete and Pact decided to conduct an additional survey in order to collect more quantitative data and verify anecdotal information previously gathered.

Due to time and budgetary constraints as well as the remoteness of the mining sites and limited staff capacity, Pact chose Magpi in order to collect the additional data. We were concerned that a paper questionnaire would require too much time to collect data, compile it and then send it to HQ.

Pact selected Magpi over other data collection methods because it responded to several key considerations. Within one week, Pact staff in the US, in collaboration with staff in the DRC, had designed the survey, trained the DRC data manager on the methodology, downloaded the survey onto the phones, trained the staff, and had them conduct a pilot survey. Pact did not have to hire local consultants or pay for non-local staff travel most likely saving the organization several thousand dollars in equipment, staff time and other costs. We were able to gather information about child labor statistics that have not been produced in any other available reports, and this information will enable Pact to design a better intervention that directly addresses the causes and socio- economic dynamics of child labor in the context of the mines.

Three unanticipated benefits of using Magpi became clear. The first unexpected benefit was the staff’s overwhelmingly positive response to the application. Staff were very excited to use Magpi and to learn how to use a new technology, that they actually carried out three times as many surveys as expected. Additional staff volunteered to take part in the research and others even utilized their personal mobile phones for data collection.

This interest and enthusiasm extended to the interviewees generating a second unanticipated bonus in that the novelty of the data collection media engaged the interest and attention of the interviewees. Communities are often fatigued by researchers and are wary of being approached with long, paper questionnaires. However, Magpi allowed for rapid recording of responses from standardized menus, which was interesting for the interviewees (mobile technology is extremely popular in the DRC) and also respected their available time.

The third unexpected benefit was the facility of centrally made edits to the questionnaire. When the field staff proactively provided feedback on the questionnaire Pact staff in the US could easily and quickly modify the questionnaire in Magpi to address these issues.

Overall, Magpi allowed Pact to collect more, better-quality data and to analyze it more efficiently. Magpi allowed Pact staff to resolve critical issues in real time and for multiple stakeholders including technical staff, managers, and data collectors, to all access the data simultaneously. Based on this trial, it will be possible to integrate the use of Magpi into more regular data collection and possibly bypass a lot of current paper entry which increases the potential for error. Pact had been considering conducting mobile data collection in the DRC for some time but there were concerns that the technology might not be feasible in the context. This data collection effort is a proof of concept, demonstrating that despite the very remote areas being surveyed and poor connectivity, mobile data collection can be enacted rapidly and effectively.