Building a Mobile-based Public Opinion Survey in Africa

Mobile Phone SurveyIn my most recent post over at the Democracy in Africa blog, I argued that using mobile phones for political opinion polls would be an effective way of identifying voter fraud as well as providing feedback for political parties. My argument sought to use the ubiquity of mobile phones in Zimbabwe to overcome the lack of landlines needed to ‘traditional’ polling and the challenges of conducting in-person surveys in rural communities. Even though we know that not everyone owns or has access to a mobile phone, with penetration almost 100%, this medium made the most sense in order to capture the most complete sample of the prospective Zimbabwean voter. Still, understanding how those who own mobile phones are different from those that don’t will be important for the generalizability of the survey. Additionally, those that respond to an SMS based poll versus citizens that do not will likely need to be explored for the purposes of generalizing the results of the survey. With these questions in mind, I’ve expanded on the the model I described at DIA to a version that would incorporate more of these variables. Below are some additional factors to include in the survey:

Sample frame:

The target of the survey would be the 13 million mobile phone subscribers in Zimbabwe. With a population just around 13 million, it can be reasonable to assume that the majority of citizens have an active mobile phone. To draw a random sample of this group in order to conduct the survey, two methods could be undertaken:

Random digit dialing: After inputing the correct prefixes for cell numbers in Zimbabwe (71, 73, and 77) a machine can dial random phone numbers. This makes it easy to replace any disconnected or unused numbers with the next random phone number.

Sample frame from the Telecoms: After partnering with the three major providers in Zimbabwe (Econet, Telecel, and Net*One), a list can be compiled of the numbers that have been active in the past 30 days from which to randomly sample. However, with many users changing SIM cards as they travel, or to take advantage of better rates, the list of active lines may include some that belong to the same person.

Weighting the Sample:

To compensate for a survey that does not perfectly represent the entire country of Zimbabwe, weights can be added to response groups in order to match the proportions known in the population. As an example, oversampling Sindebele speakers (who are only around 15%) or urban residents (5 million of the 13 million in the country) could be reproportioned to compenstate. This would not help if the sample itself is unrepresentative, but for the purposes of this project, the sample of mobile users is assumed to be the best representative sample that can be achieved.


To make sure that the survey was successful it would need at least one round of testing (a pilot program) to flesh out the right wording of the questions and the right incentive for response. Usually this is done by running the survey twice with only one difference in the implementation. One group could be asked about their ethnicity or language, and the other would not, in order to see if the overall response rate dropped because people were put off by the question. Other pilots could test the level of incentive needed to ensure a high response rate. Does 25c of airtime make it worth someone’s time?

Finally, what information people are told about the survey before they participate will be crucial to gauge. While Americans are used to pollsters calling for their opinion and assume that their anonymity will be protected, this can not be said around the world. Does Billboard in Mozambiquea public campaign need to be undertaken? Can a SMS with a url link to a website do the trick? Understanding what information is needed by those surveyed to have enough trust in order to respond will be one of the most crucial steps taken.


With these factors in mind, the formation of a mobile-based public opinion survey in Zimbabwe could be begun. Generalizability will be a crucial component for this survey as it’s likely to be one of the most critiqued factors. Those that respond to the survey could be   less fearful that their answers might offend the security regime, and thus more likely to be ZANU-PF supporters. Conversely, those that do not own mobile phones are more likely to be poor and rural citizens, and are a group that ZANU-PF has consistently targeted for electoral support. These plus other factors need to be overcome by the structure of the final form of the survey in order to produce a non-response rate of less than 30%. Any more and there’s no chance of generalizing about the voting habits of the Zimbabwean populace. Despite fine tuning the sample frame, correctly weighting the demographics, or identifying the right incentive, Zimbabweans may still be resistant to mobile polling. If the risks perceived by a voter in revealing their political preference cannot be assuaged by those conducting the survey, then high measurement error will likely result.

Despite the challenges and difficulties in achieving a proper survey, there is a clear social benefit provided to the Zimbabwean people. Polling allows a population to give instant feedback to political parties and candidates during elections. While the United States has become obsessed with polling to the point of superfluousness, citizens are able to indicate their preferences at various times, rather than just through a one-off election. Additionally, election monitors can use the trends of polling to provide a more accurate assessment of whether elections have been rigged or manipulated. If monitors know that Province A had been trending support of 60% for candidate A but in the election candidate B receives 70% of the vote, a red flag can be triggered and thus alert monitors to further investigate the results. Without knowing pre-election opinions, no one can be certain if the populace truly voted the way they did.

Democracy is at a standstill in Africa despite recent headlines. Technology will not be a democratizing agent on its own. However, if used in certain circumstances to improve accountability, transparency, or connectivity, we should embrace the chance help consolidate existing republics as well as bringing forth more democratic regimes in Africa.

Flag Map of Zimbabwe

This article is an addendum to an article posted on Democracy in Africa

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African Week in Review Feb 11-17

Only one place to start this week in review, and that is in Libreville where Zambia defeated Cote d’Ivoire on kicks from the spot at the end of extra time. I remember reading about the Zambian air tragedy when their entire soccer team went down in the Atlantic after taking off from Libreville, and was it was interesting that picked up on this before the tournament and interviewed Emmanuel Mayuka who became Zamibia’s talisman during the tournament. It is terrible that the tournament was not shown on American television, as the story lines in the tournament would have made for a great example of an African redemption story.


  • So, I found myself rooting for Zambia on Sunday, despite telling anyone that would ask (and some that didn’t) that this was Cote d’Ivoire’s year to win the Africa Cup of Nations. In the end, the braver team won (but perhaps it was preordained?). If you looked into the eyes of the Ivorian penalty takers, they were scared to miss more than the Zambians. Now, Drogba’s missed penalty in normal time certainly didn’t help the confidence, but in the biggest game on the African continent, you have to be ready to psych yourself up to take a kick from the spot. Anyone could see that Gervinho kept looking to the ground, and made no eye contact with anyone during the kicks. Some might say that when it comes to the 9th taker, you can’t blame the guy because he was forthright in saying he didn’t want to take a kick. But when you’re a star player on the 15th ranked team in the world, you need to be ready to step up in the crucial moment for your country….
  • Check out the video highlights of the kicks from the spot on youtube while Eurosport leaves them up! via Football is Coming Home
  • Below is a breakdown by The Economist on the possible influence (or lack thereof) by European based African players.

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The African Cloud: Predictors for Adoption of Cloud Computing

My most recent paper got some twitter promotion by some of the big names in the ICT world. First, my paper got into @ICT_Works and was published in their ICT4D Daily:

Then later that day, a website that I’ve found to have some amazing content during my ICT and Democracy research, @MobileActive, retweeted my post:!/mobileactive/status/71244340407713793

Between these two Twitter feeds, there are 10,000 followers. When I saw that they had retweeted my story, I expected a flood (or at least close to 100) hits coming through the link to my most recent paper, The Cloud and Africa – Indicators for Growth of Cloud Computing. Alas, not a single follower (according to WordPress’ statistics) of those twitter feeds clicked on my link to read my paper.

The reason I had been so excited about this paper  was the originality of the idea. I had never written a paper that was so quantitative heavy, but I had to go that route after I found the literature of Cloud Computing to be limited in the development sense. The few mentions of cloud computing in developing markets focused on India and China. So with the limited amount of data, I had to hypothesize and use what I had learned in class to make a prediction. One of the biggest things I’ve learned while being back in academia the past year is the proliferation of the fear of prediction making. So I decided to buck this trend and create a database that would predict which countries in Africa were most likely to embrace cloud computing as a development and economic solution. What resulted was a table that formed the basis for my Cloud Readiness Index. Certainly, it is not anything that would withstand academic review, but something I thought that could be a good conversation starter and one that could provide information to policy makers and businesses on which markets to examine further to determine if they would adopt the model of Cloud Computing.

My class presentation is here.

The table can be seen here.

My paper is linked to here