Using Communications Technology to Promote
Democratic Participation: Experimental Evidence fromSouth Africa
Aaron Erlich, Karen E. Ferree, Clark C. Gibson, Danielle F. Jung, James D. Long,
and Craig McIntosh∗This version: November 2022

Abstract
We present a national-scale effort to use technology to promote political participation in an emerging democracy, South Africa. A multi-channel digital platform registering over 90,000 South African
citizens conducted opinion polling, crowdsourced information on electoral activities, and enabled
citizen monitoring of polling places during the 2014 general election. Different channels display
starkly distinct demographic profiles and patterns of engagement. Sequential experiments withrandomized extrinsic incentives illustrate complementarity with the intrinsic engagement of citizens to participate on the platform. Our results illustrate how low-cost communications technology
can ethically be used to promote political participation.
JEL Codes: D72, C93, O30.
Keywords: Elections, Technology, Randomized Experiments.

∗

We acknowledge generous funding from the U.S. Agency for International Development (USAID), Development
Innovation Ventures (AID-OAA-A-14-00004). The project was registered and filed a pre-analysis plan with EGAP in 2014prior to the initiation of the study at https://osf.io/ac69z. We thank Sarah Bush, Mike Callen, Wes Day, Adam Glynn,
Guy Grossman, Juan Carlos Guzman, Nahomi Ichino, Brian Klaas, Michael Kremer, Scott Mainwaring, Victor Menaldo,
Dan Nielson, Maura O’Neill, Robert Pekkanen, Dan Posner, Lauren Prather, Alex Riehm, Molly Roberts, George Tsebelis
and seminar participants at the University of Washington, Paris School of Economics, George Washington University,
London School of Economics, Princeton University, Notre Dame, Working Group in African Political Economy, the World
Bank, and USAID for comments. Kate Blackwell, Grant Buckles, Alex Verink, Damien Willis, and Stephen Winkler
provided excellent research assistance. All mistakes remain with the authors and any opinions, findings, and conclusions
or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of USAID.
The project received Institutional Review Board approval at the University of California, San Diego. Corresponding
author: McIntosh. ctmcintosh@ucsd.edu. Replication data posted at https://doi.org/10.7910/DVN/OX14OX

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1Introduction

Mobile technology has become a major driver of the way in which democratic elections are conductedworldwide. From the influence of new media coverage on elections (Prior, 2005) to technology’s
coordinating role in the development of fragile democracies (Fafchamps, Vaz and Vicente, 2020;
Shirazi, Ngwenyama and Morawczynski, 2010; Tufekci and Wilson, 2012), there is no question that
information and communications technology (ICT) is now a central force in the democratic process.
Much of this change is positive: ICT provides new means for citizens to monitor bureaucrats(Banerjee et al., 2020), a new vehicle to understand the political process (Tufekci, 2014), and evena means to improve the conduct of elections themselves (Callen and Long, 2015). However, in
recent years more attention has swung to the downsides of this change, including the potential of
the echo chamber of new media to suppress voting in developed countries (Falck, Gold and Heblich,
2014; Gavazza, Nardotto and Valletti, 2019) as well as the propagation of misinformation and theamplification of extremist voices in political dialogue (Allcott and Gentzkow, 2017; Gorodnichenko,
Pham and Talavera, 2021; Morozov, 2011; Rid, 2020), potentially leading to violence (Pierskalla
and Hollenbach, 2013). Particularly given a recent observational literature suggesting that ICT
may actually have increased participation in developing countries (Donati, 2019; Manacorda andTesei, 2020), rigorous evidence from the Global South is critical.
In this paper we report on the results of a mass-scale effort to drive positive forms of participationin the 2014 South African general election using a novel combination of ICT tools. To test theability of technology to promote engagement, we built a multi-channel platform called VIP:Voicethat was accessible from a range of devices including a simple feature phone interface, two of South
Africa’s major homegrown social media networks, and connections to international social networks
such as Twitter and Google Chat. We promoted the platform by sending out 50 million text
messages and then conducted a series of subsequent participation experiments using the 90,000
participants who registered on the system. We aggregated polling data and distributed it throughthe platform, but did not allow users to post content directly. By building links to social media
platforms but then strictly controlling the dissemination of information, we sought to understandhow to induce constructive engagement in the electoral process while prohibiting the disseminationof misinformation. The two main contributions of the paper are a) the descriptive effort to build anational-scale platform to support electoral transparency, and b) analysis of a series of embedded
incentivization experiments that let us speak to the dynamic relationship between subsidies andthe intrinsic motivation to participate in political activity.
From a policy perspective, we may hope for ICT to engender a number of specific forms of1

democratic engagement. First, opinion polling on mobile phones can reach a mass audience andachieve more representative results (Rivers and Bailey, 2009), particularly in countries with poor
landline penetration, and lacking registries of mailing addresses (Hoogeveen et al., 2014). Second,
given the recent ubiquity of mobile phones even in some of the poorest countries on earth, ICT is anatural conduit for Get Out the Vote (GOTV) efforts (Grácio and Vicente, 2021; Green and Gerber,2019). Third, a recent literature has shown how citizens armed with mobile phone apps are able tomonitor polling places on election day, ensuring that the electoral commission records vote tallies
in line with those posted on the ground (Callen et al., 2016). Finally, crowdsourcing techniques canbe used to permit reporting of irregularities such as violence or vote-buying, providing a geographic
reach difficult to achieve with professional election monitors (Findley et al., 2012; Van der Windt
and Humphreys, 2016).
Our results demonstrate the promise of pro-participation ICT interventions to reach mass scale,but also show the fragility of participation as individuals move from relatively costless forms ofdigital participation into more costly real-world engagement with elections, such as voting or monitoring their polling places. Beginning from over a quarter million initial contacts with our platform, participation drops with every step of engagement; over 90,000 individuals sign the Terms
and Conditions of the platform and complete registration, almost 35,000 provide demographic andprior voting information, 16,600 provide additional information via opinion polling or reportingof political events in the runup to the election, 2,500 volunteer to monitor their polling places onelection day, 330 actually do so, and 11,000 provide information on voting conditions on election
day. Hence, the platform recovers impressive numbers of citizen reporters, but also suffers attritionon the order of 50% for every additional action that users are asked to take.
The demographic profiles of users across different technology channels are starkly different;
almost two-thirds of users on standard flip phone channels are Black women, while users of social
media channels are predominantly male and higher fractions are Coloured (the South African term
for mixed-race individuals), while Twitter slants towards white and Asian users. Hence, the detailsof the choice of technology channel have dramatic effects on the profile of users. Daily opinion pollingin the weeks before the election shows that different channels have non-representative politicalpreferences in a manner that is predictable based on demographics, but given the rich variety ofuser types across the channels we would be able to weight them so as to achieve representativenessin concert with the high-frequency of real-time ICT-based opinion polling. Our unique effort tofield a comparable platform across multiple channels allows us to speak in a very straightforward
way to the linkage between technology, user profiles, and the types of data that such platforms canrecover.

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We implemented a sequential set of incentive experiments that let us speak to the complex
interplay of extrinsic and intrinsic motivation in the provision of political participation in a novel
applied context. In this environment, intrinsic motives are the set of factors that induce individuals
to participate in the platform or the political process in the absence of any incentivization, andthe extrinsic incentives are provided through the platform in the form of free airtime, lottery
incentives, and direct payments to serve as Citizen Monitors. The behavioral literature has longpointed out the potentially corrosive effect of financial incentives to undertake a task in context
where individuals were already intrinsically motivated to do so (Benabou and Tirole, 2003; Gagné
and Deci, 2005; Gneezy and Rustichini, 2000). Extrinsic incentives undermine the motivation
to engage in pro-social activities if agents wish to be seen to be “doing good rather than doingwell” (Ariely, Bracha and Meier, 2009), and have even been shown to color the way that individuals
assess political candidates (Isbell and Wyer, 1999) and vote (Gonzales, León-Ciliotta and Martı́nez,2022). We move this mostly lab-based conversation to the field by asking individuals to engage ina number of pro-social activities (volunteering data on themselves and the political environment
in the run-up to the election, serving as Citizen Observers at their polling places on election day),with randomization both in the incentives to come on the platform in the first place and of the
incentives to engage in subsequent, high-cost behavior. We have three different ways to consider
how extrinsic motivation intersects with the inherent desire to participate in the political process.
First, a number of lab experiments have found that small monetary incentives actually discourage
participation (Gneezy and Rustichini, 2000) and only larger incentives are net positive. Because
we offer only small incentives, if this were the case in our real-world context we would find lower
participation in the incentivized arms. Second, we have randomized variation on the extensive
margin arising from the fact that some of our user groups (particularly the Lottery USSD group)
register at higher rates because of initial extrinsic motivation, and hence we can ask how thisdifferent composition of users then drives subsequent intensive margin sensitivity to incentives.
Finally, we can use the response to our initial Engagement question on a user’s level of intrinsic
motivation around the election to examine heterogeneity in the response to extrinsic motivation
based on the level of inherent political engagement.
Overall, we find several distinct pieces of evidence suggesting that intrinsic and extrinsic motives
are complementary in this context. Small incentives never depress participation and only (weakly)
enhance it, suggesting that extrinsic incentives are not crowding out inherent motives. We find thatthose who initially were randomized to receive incentives to participate are more malleable, in thesense that they are more responsive to subsequent incentives for digital participation than those
who joined without incentives (although this interactive effect disappears as we ask individuals to3

take costly real-world actions). Finally, not only do incentives have no effect on individuals whoreport low intrinsic motivation for political activity, but the differential effect of incentives for thehighly motivated are the largest for the channels where intrinsic motivation was highest in the firstplace (even when we control for demographic differences across channels). In this context, extrinsic
incentives do not undermine intrinsic motivation and may in fact only work for those who are
intrinsically motivated. We return to a discussion of this (potentially surprising) complementarity
in the Conclusion.
Finally, we examine the success of the platform in engendering actual citizen monitoring ofpolling places on election day. We were able to recruit citizen volunteers willing to observe 12%of the polling stations in 37% of the wards in the country. Of the 1,830 individuals we invited toperform monitoring, 332 did so. Again, the crowd-sourced approach to election monitoring displays
both upsides and downsides. While we are able to achieve broad geographic presence of citizen
monitors at very low marginal cost, the likelihood of recruiting a monitor in any given location islow, and the data quality of the reports submitted in the absence of any training is poor. We findextrinsic incentives to be effective at increasing both response rates and data quality for citizen
monitoring, however, and a cost-effectiveness analysis suggests that on the margin, incentivized
citizen monitoring of polling stations can be an attractive way to achieve scaled cross-checkingof local vote tallies. Overall the project suggests a path forward in terms of ways to engineer
ICT-based platforms in a manner that engenders beneficial forms of political participation without
becoming a conduit for misinformation.
This study joins a growing literature trying to understand how technology can induce participation in the political process. This question is key, in that fairer elections are more likely tokeep leaders accountable to the electorate (Besley and Burgess, 2002; Downs, 1957; Fujiwara, 2015;
Przeworski et al., 1999).1 The rapid adoption of ICT by citizens in developing countries represents
a dramatic change in their capacity to communicate and mobilize for action (Alozie, Akpan-Obongand Foster, 2011; Bratton, 2013; Shirazi, 2008). The literature on democratic engagement has
shown the promise of ICT to improve electoral integrity (Bailard and Livingston, 2014; Callen et al.,2016); crowd-source information on violence, corruption, and government performance (Cecchiniand Scott, 2003; DeRenzi et al., 2011; Findley et al., 2012; Pierskalla and Hollenbach, 2013; Shapiro
and Weidmann, 2015; Van der Windt and Humphreys, 2014; Weidmann and Callen, 2013); andstrengthen accountability between citizens and politicians (Grossman, Humphreys and SacramoneLutz, 2014). The broad reach of the technology has also illustrated the ability to increase the1

The low cost of gathering data through mobile phones has led to widespread use of ICT in survey research (Vicente,
Reis and Santos, 2009) and crowdsourcing (Van der Windt and Humphreys, 2016), including a very important role during
the COVID epidemic when face-to-face fieldwork was impossible (Gourlay et al., 2021).

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political engagement of marginalized populations such as the poor, those in peripheral regions, andwomen (Findley et al., 2012; Grossman, Humphreys and Sacramone-Lutz, 2014).
We also complement a rich body of policy evaluations in international development focusedon governance, accountability, and transparency (Ferraz and Finan, 2008; Olken, 2007), including
the opportunities and challenges of democracy promotion through electoral assistance (Hyde, 2007,2011; Kelley, 2012). Donors and governments commit significant resources to support elections especially as an important mechanism for state-building, building inclusive institutions, and spurring
economic growth in conflict-affected, transitioning societies. Problems of election administration
to deliver a credible results continue to undermine many races (Donno, 2013).2 The South African
Independent Election Commission (IEC) had to hire, train, and deploy 250,000 poll workers simply
to run the election. We therefore view platforms like VIP: Voice as important instruments to bolster
and complement the efforts of other groups and organizations.

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Context and Motivation

2.1

Setting: The 2014 South African Election

South Africa provides a compelling setting for a study of technology-driven political participationin an emerging democracy. It is a diverse and highly unequal country, with a technology landscapethat spans a high-income urban population using technology as in the Global North, alongside alarge rural population whose use of flip phones closely resembles the rest of Sub-Saharan Africa. The
country is also an innovator in the use of ICT in the African context, providing strong in-country
counterparts and making it an important test case for the use of ICT in democratic engagement.
1994’s transformative elections brought an end to Apartheid, allowing universal franchise andenergizing democratic participation on the part of the non-white majority (Johnson and Schlemmer,
1996; Reynolds, 1994). Since then, the ruling African National Congress (ANC) has won national
contests with consistently wide margins, greatly outpacing its nearest competitor, the Democratic
Alliance (DA); other smaller parties had not gained much traction by 2014, at the time of thisstudy (Ferree, 2011). The ANC’s dominance limits political competition, potentially discouraging participation since elections are seen as foregone conclusions. Turnout for national elections
dropped nearly 30 percentage points from 1994 to 2014, with lowest rates in the youngest groupsof eligible voters (Schulz-Herzenberg and Southall, 2014). But while election turnout may be in
For example, the international community spent over $250 million to administer Afghanistan’s 2010 election, $147
million on the 2014 election, and parliamentary and presidential elections slated for 2018-19 were each projected to cost atleast $210 million (Adili and van Bijlert, 2017). The Kenyan government spent upwards of 1 billion USD to purchase a newcomputerized results transmission system in 2017 to guard against hacking into the commission’s reporting mechanisms.
2

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decline, South Africans have a long tradition of political activism. Demonstrations and riots werecommon features of the anti-apartheid era (Lodge, 1983; Lodge and Nasson, 1991). More recently,
South Africans staged widespread protests against the state for its poor record of delivering basic
services (Alexander, 2010; Southall, 2014). Since 2008, more than two million South Africans haveparticipated in service delivery protests (Plaut and Holden, 2012). Thus, South Africa, like many
emerging democracies, has a record of uneven political participation.
The South African Independent Election Commission (IEC) has been vulnerable to fraud andelectronic results “hacking” in previous elections (Harris, 2011). Today, however, South Africa’s
election environment is largely viewed as free and fair and a model for other developing countries.
The IEC is considered one of the best performing election commissions in Africa (Ndletyana, 2015)
and citizen election monitoring is protected by law (Ndulo and Lulo, 2010). Nonetheless, in approving VIP:Voice, the IEC expressed the need to improve their administrative functioning overelection duties at the country’s roughly 23,000 voting stations, operated by nearly 250,000 temporary workers, given managerial and technological limitations to oversight and monitoring even in
the absence of industrial-scale fraud.
In technological development, South Africa has enjoyed a tech boom in recent years. It boasts
the highest cellular phone connections per capita in Africa3 and the fifth highest internet accessrate. Cell phone saturation was almost 90% in the 2011 census and has since risen to almost 100%.
Web-enabled feature phones and smartphones currently have a saturation rate of 70%. Moreeconomically developed areas of South Africa have higher usage rates, as do younger and moremale populations (see Table ??). The fact that the country combines a large poor population withaccess to the same technology as most of the continent (flip phones) in concert with a wealthier,urban population that use smartphones and social media makes it an ideal environment for themulti-channel intervention we host. In terms of the mix of devices used by its citizens, South Africa
in 2016 had a technology blend that looks like much of the rest of Sub-Saharan Africa today.

2.2

The VIP:Voice Platform

We worked with Praekelt, a South African technology firm, to design and promote a system called
VIP:Voice on three distinct channels. First, it could be linked to and promoted via internationalsocial media channels such as Twitter and GTalk (Google’s Talk platform, in relatively widespread
use among wealthier South Africans at that time). Second, we built an interface and a promotion
strategy to link into two South African social media networks. The first of these, Mxit, was thedominant indigenous social media platform at the time, with over 7 million subscribers in 2013.
3

As of 2014, 149 connections per 100 citizens; Nigeria had 77.84 per 100 (World Bank, 2014).

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The second, Mobi, was a more specialized mobile platform focusing on the youth market. Finally,
and most importantly in terms of its potential reach, was a platform designed to work over feature(non-smart) phones. Because the large majority of South Africa’s poor did not use smartphones
at the time of the study, it was key to design an interface to work with standard handsets. We
used the typical technology for these phones, which is Unstructured Supplementary Service Data
(USSD). This opens a 90 second window to the user during which they can navigate through adecision tree by entering numbers on their phone and respond to prompts using either drop-downmenus or free-form text responses. While USSD is less convenient to use than a smartphone app,it was in widespread use by telcom and mobile money providers and was very familiar to flip-phoneusers in South Africa.
While there were inevitable differences in the appearance and operability of VIP:Voice acrosschannels, we made every effort to keep the user experience as similar as possible. The most technologically sophisticated channels (those that required feature or smartphones) were also the easiest
to use; in contrast, the more rudimentary SMS/USSD channel, available on even the most basic
phones, was the clunkiest. Difference across these channels thus operationalize the concept of technology change: the SMS/USSD channel represents a high cost/hard to use technology channel andMxit or Twitter represents a low cost/easy to use technology channel. This range of user costs isrelevant to the technology landscape in many developing countries today feature a similar range
of user devices, with poorer citizens interacting with ICT using channels that impose higher costs
than internet-enabled smartphones.

2.3

Research Design

The operation of the platform took place in three distinct phases that transpired over the course of
40 days in April and May of 2014 (the platform launched on April 7th, and the election was May
7th). These phases were: (1) promotion of the VIP:Voice and user registration in VIP:Voice; (2)surveys of political engagement, voter preferences, and electoral issues in the run-up to the election;(3) inducement to participate as an election monitor on election day, as well as surveys of voting
and voter experience on and after election day. Phases 1 and 2 can be considered periods of lowcost,“digital” participation (ICT recruitment in Phase 1 followed by digital engagement with ICT
in Phase 2), and Phase 3 is high-cost, “real-world” participation. Figure ?? provides a summary
timeline of the sequence of phases relative to election day, and Figure 1 displays the research
design of the overall project, showing the temporal division of the study into phases with blue lines
representing experiments, including incentives to register in Phase 1 and additional experimental
components in Phase 3.

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2.3.1

Phase 1: ICT Recruitment

In Phase 1, starting on April 7, 2014 (one month before the election), we began recruitment of
citizens into VIP:Voice. Unlike the large number of ICT studies that rely on pre-drawn samples,
we attempted to recruit a user pool entirely by digital means. To reach the largest possible groupof potential participants in Phase 1, we focused heavily on SMS/USSD interactions, which have thewidest penetration, including in rural areas where other digital media did not have the same reach.
We recruited people to this channel primarily through advertising with so-called “Please Call Me”(PCM) messages. Facilitated by telecom providers, South Africans send an average of 14 million
overall unique PCMs per day. Senders text a PCM to a recipient, requesting a return phone call.
The recipient of a PCM sees the number requesting a call as well as an ad. Advertisers pay for
PCMs, not senders. As far as we know, VIP:Voice is the first major platform of its kind to use
PCMs as a recruitment tool. We purchased ad space for VIP:Voice for 49.8 million PCMs.
The PCM outreach was used to conduct an experiment in user recruitment. The “standard”
arm encouraged registration but required users to pay full messaging costs; the “free” arm removed
the user fees so that interaction with the platform bore no cost, and the “lottery” arm offering achance to win R55.4 See Appendix ?? for the exact text of the PCM message in English, Afrikaans,
and Zulu. Within the USSD channel, then we have an extensive margin experiment in terms ofuser groups that were engaged with different extrinsic incentives, and then retained different costs
for some parts of their subsequent interaction with the platform (to be described in more detail in
Phase 2).
We also anticipate the cost and lottery treatments may affect participation in different ways.
Both are forms of extrinsic reward, and we expect both to increase participation relative to the“standard” USSD treatment (barring net crowd-out). However, the free treatment offered a cost
reduction (R0.2, about 1.5 US cents per USSD session) with certainty while the lottery treatment
offered a probabilistic reward of R55 or 5.17 US Dollars, where participants were not explicitly
given a probability but told ‘enter for a chance to win’. The research team assigned a budget
to cover lottery payments but we do not know the exact probability of payouts ex post. For thelottery treatment to supersede the free treatment in expected value, agents would have to assume arelatively high probability of lottery payout (greater than 1 in 275). As this is arguably an unrealistic
assumption for most real-world lotteries, a rational agent might respond more to the offer of freeservice. On the other hand, R0.2 is a trivial amount, even for relatively poor participants, and many
4

The text of the PCM message always read “Join VIP:Voice to help make elections 2014 free and fair. Dial ...”.
The standard treatment said “Standard rates charged,” the free treatment said “participate for free,” and the lottery
treatment said “stand a chance 2 win R55 airtime”.

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prior studies in behavioral economics have shown that agents tend to over-weight small probabilities(Camerer, 2004; Kahneman and Tversky, 1979). For these reasons, a lottery, even or especially
one without the odds given, may have a stronger effect on behavior. We treat this an empiricalquestion.
Then, over the social media channels and the Twitter/GTalk channel, the platform was promoted through the purchase of standard advertising (see Figure ??), and users could enroll into
VIP:Voice through native links within the apps. Users entering through these channels by definition had smartphones and so the platform was easier to operate, did not ‘time out’ after 90
seconds as the USSD channel would, and could more easily find parts of the platform that required
user-directed navigation rather than ‘push’ messaging from SMS. There were no recruitment experiments performed on these channels, and so the study user groups are most naturally thought ofas consisting of three experimental strata (USSD Standard, USSD Free, USSD Lottery), and two
endogenous strata (Social Media, Twitter/GTalk). The USSD channel was only offered in English,
but the Social Media channel also had a Zulu and an Afrikaans option that users could select on
entry to the platform (English was the default). Because this option is universally observed forusers of Social Media channels and generates a very distinct profile of users, in much of our analysis
we further break out the Social Media channel into its linguistic sub-groups.
On entering the system, users from all channels were asked a teaser “engagement” question
about their voting intentions in the election. See Figure ?? for a screenshot of how the engagement
question appeared on the USSD interface.5 They were then asked to sign Terms and Conditions forthe platform. The Terms and Conditions for the platform were a combination of the standard commercial language that would be used by Praekelt for digital platforms, and the Institutional Review
Board language required for a research study, identifying this as a project being conducted by the
University of California and providing information on anonymity of data and contact informationin case of concerns. Having signed the Terms and Conditions, users were considered ‘registered’and handed off to Phase 2 activities.

2.3.2

Phase 2: Digital Engagement

Phase 2 invited registered individuals to participate in three types of digital engagement, each ofwhich had distinct outreach and incentivization structures. The first of these was a short Demographic survey. Immediately upon registering and answering the Engagement question, all users
were directed to these questions via a splash page that said ‘Answer and Win!’, meaning that a
5“It’s election time! Do u think ur vote matters?” Response options included, “YES, every vote matters,” “NO, but
I’ll vote anyway,” “NO, so I’m NOT voting,” “I’m NOT REGISTERED to vote,” and “I’m TOO YOUNG to vote;”.

9lottery incentive was provided to all users for the Demographic questions regardless of channel
or original PCM treatment status. This survey attempted to capture the gender, race, and ageof respondents as well as whether they had voted in the 2009 general election (see Appendix ??
for text). At the end of this survey, individuals who had entered from the social media/Twitterchannels were also asked if they wanted to provide their phone number. Those who did were thenincluded in all subsequent ‘push’ treatments sent out via SMS, along with all USSD users.
The second Phase 2 activity was a set of passive What’s Up survey questions that could be answered by any user at any time, but were never pushed to the user via SMS or on the smartphone
apps. These questions asked about the performance of national and local governments, whether
users were attending protests or rallies, trust in different political parties, incidence of voter intimidation, and intention to vote (see Appendix ??). These questions were never incentivized for users
regardless of channel or PCM treatment status. Only a vanishingly small fraction of USSD users
ever navigated to these questions, and so the overwhelming fraction of responses to these questions
come from social media users.
The third cluster of activities in Phase 2 consisted of a set of Push questions sent out to allusers by SMS, regardless of the channel on which they entered. We had the telephone numbers
for all USSD participants by definition, and for registered Social Media users we solicited theirphone numbers and included the 11,528 or 26% of them who did so. The Push survey consistedof five sequential questions sent every other day over the course of nine days with the initial startdate randomized; the second and fourth question was always an opinion polling question about the
party for whom the user intended to vote, and the remaining questions were randomly selectedfrom a bank including information about political rallies, political violence, vote buying, voter
intimidation, campaigning, and protest activity (see Appendix ??). For these questions all users
retained their original randomized incentivization structure (standard rates for the Standard andsocial media users, free for the USSD Free arm, and lottery for the Lottery arm).

2.3.3

Phase 3: Real-world Participation

In Phase 3 we sought to enlist citizens into meaningful and costly real-world forms of politicalparticipation; namely voting, performing citizen monitoring of polling places, and reporting on
irregularities that had been observed while voting. Again, this phase consisted of several distinct
components with multiple layers of experimentation. The first of these was a Get Out the Vote
(GOTV) campaign that randomized the message used to induce individuals to go to the polls.
Because we do not have a credible measure of actual voting at the individual level, we do notemphasize this GOTV experiment in the discussion of results.

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The most pivotal Phase 3 activity was an effort to recruit a group of engaged users of the
VIP:Voice platform to monitor their polling places on election day. We began with a group of“high compliers” who had interacted with the platform during Phase 2, and from within this group
we asked a subset of individuals to serve as Citizen Observers (COs). The tasks expected of COs
involved returning to polling stations on the day after the election to observe whether or not a
declaration of results form (tally sheet) had been posted, submitting information about the tally
via SMS, and taking a photo of the tally if equipped with a camera-enabled phone via photo quick
count, all protected by South African law (see Appendix ?? for text, and ?? for a screenshot of theappearance of the solicitation on USSD ).6 By reporting information from the tally sheet, a CO also
makes it possible to evaluate whether local posted results match centrally reported results (Callen
and Long, 2015). This represents a tangible election observing activity a citizen can reasonably(and safely) participate in that provides useful information about the adherence of local polling
stations to electoral procedures. Hence, these activities provide valuable ways in which ordinary
citizens can participate meaningfully in observing electoral quality. This is a form of real-world
participation and consequential citizen monitoring of government actions.
We randomized an extrinsic incentive to volunteer as a CO, randomized as either a token
amount of R5 to cover phone fees or a more substantial inducement of R50. Those who indicatedan interest in serving as COs received a new set of Terms and Conditions (T&Cs) to accept andprovided personal information to allow us to identify their polling stations. We subsequently refer
to “CO volunteers” as those who volunteered as COs, signed new T&Cs, and provided personal
information. We invited 50,995 participants to volunteer as COs, of which 2,507 agreed, signed
T&Cs, and provided all relevant location information required to identify their polling place. We
were thus able, through the platform alone, to recruit citizens willing to observe 12% of the polling
stations in 37% of the wards in the country.7 This experiment is particularly interesting giventhat it is cross-cut against the PCM experiment, which generates extensive margin variation in the
degree of extrinsic motivation of users on the platform, and comes after the Engagement question
which can be used as a measure of intrinsic motivation.
Our original design was to field these volunteers on the day after the election through additional
messaging and instructions. Due to a data error, however, the platform sent these additional
messages and instructions to an entirely different group of 1,899 individuals (who had not previously
volunteered to be CO volunteers but were still registered in the VIP:Voice platform) asking them
6
Similar to many countries, electoral law in South Africa requires posting of tally sheets by polling center managers.
Posting of sheets improves electoral transparency, allowing voters to observe their local result.
7
We purposefully limited this activity’s reach as way to demonstrate proof of concept of citizen-monitoring and photo
quick count. We also required locational information, which was hard to process at the time (see also Erlich et al. (2018)).

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to observe the voting tallies the day after the election; of these unwitting volunteers, 350 submtted
information via SMS about their polling stations. This second group were almost exclusively
registered USSD participants in the USSD Standard arm. These COs were also offered one oftwo different incentives to complete their tasks (R5 or R50), and assignment to these incentives
was as-if random.8 This variation arose as a result of a data error and was not strictly controlled
by the researchers, however it does not appear to be correlated with user attributes within the
USSD Standard arm. We therefore present results on the randomized impact of incentives in theinvitation to volunteer, and on the non-experimental effects of the actual request to monitor on
actually monitoring, which should be interpreted with more caution.
Finally, in the days after the election we used SMS to push out a Voter Experience surveythat asked voters about the time they waited to vote, whether they encountered correct voting
materials at their polling places, any intimidation or violence, and their confidence in South Africa’s
Independent Electoral Commission (see Appendix ?? for survey questions and Figure ?? for thephone interface). These questions retained the original incentivization treatment status of Phase
2. VIP:Voice stopped contacting users one week after the election.

3

Results

3.1

The Waterfall of Participation

The overall waterfall of participation in the platform is summarized in Table 1. The total Phase 1
recruitment effort, including close to 50 million PCM messages, logged roughly 263,000 individuals
contacting the platform, 134,047 responding to the initial teaser engagement question, and 90,646
completing the Terms and Conditions. The ‘Free’ treatment had a registration rate very similar
to (in fact slightly lower than) the ‘Standard’ arm, suggesting that the cost of interacting with a
USSD platform is sufficiently low that it does not impose a meaningful barrier to participation.
The ‘Lottery’ arm, on the other hand, more than doubled the enrollment rate to .12%, or 1 per 800
PCMs.9Just under half of registrants entered through the PCM-linked USSD channels. A similar number
entered via Mxit, and the remainder of those we term ‘social media’ users came in through Mobi.
8

See Appendix Table ??.
Because multiple PCMs may be sent to the same person, we cannot define uptake in the usual way for this experiment.
Rather, we divide registered users by the number of PCMs sent under each treatment to calculate a yield rate, implying
an average yield rate of .053% per PCM for the USSD channel when users had to pay the full cost of the interaction,or 1 in 1,900 PCMs. This cannot be interpreted as a standard yield rate because PCMs may be sent many times to thesame person and the same individual may have received PCMs with different treatment statuses. What we show here is
the yield per PCM, not the rate per person sent a PCM. These effects are internally valid but the treatment effect of one
intervention may be altered by the presence of the other PCMs being sent at the same time.
912

Only a very small number entered via GTalk or Twitter. USSD users who enrolled in the program
directly rather than by PCM may have come from print advertising or heard about the platform
through other channels but registered on a phone. This self-enrolled USSD group is not used inany experimental analysis because PCM treatment status cannot be assigned. The default Englishsocial media channel comprises 80% of the users on Mxit and Mobi, but 13% of social media users
elected to interact with the platform in Zulu, and 7% in Afrikaans.
Attrition continued in Phase 2. Of the 90,646 people registered, 34,727 (38%) completed thefour demographic questions and 15,461 (17%) answered the demographic questions and one of theother four Phase 2 surveys. 1,775 individuals volunteered to serve as Citizen Observers at theirpolling places, and 331 actually did so. Almost 7,000 individuals filled out post-election surveys on
conditions at their polling places.
These participation numbers are impressive and sobering in equal measure. On the one hand,
after the Phase 1 recruitment drive, more than a quarter million South African citizens initiatedcontact with the platform; more than 100,000 of these citizens provided information of some sortand over 90,000 registered into the system (in Phase 2), and 2,500 people completed all the required
information and registered as COs (activated for Phase 3 monitoring). On the other hand, thisrepresents a tiny fraction of the individuals originally approached with PCM messages, and attritionat every step of the process—from contact initiation, to the enthusiasm question, registration,
answering any of the Phase 2 questions, and volunteering as a CO—is on the order of 50% per
step. No matter how large the sample of participants, and even in the absence of any subsequent
attrition, this type of purely digital sample recruitment is likely to yield a sample of users that is
not rigorously representative of the population.

3.2

Demographics of Participation

The advantage of our PCM recruitment strategy is that it allowed us to reach a wide base of
potential participants throughout South Africa—in essence anyone with at least a standard cell
phone. A major disadvantage is that we only have information about participants if they provided
it to us within the platform in Phase 2. Hence, even in the simple descriptive endeavor of comparing demographic characteristics across technology channels, attrition across responses presentsa challenge: we can only compare attributes of those who agreed to give us their demographic
information, which we cannot assume is random across channels and therefore may differ fromthe true (unobserved) distribution. Recognizing this issue in advance, we put the demographics
questions immediately after enrollment and always incentivized their responses for all users. In theend we recovered demographic data for 35,000 people, and we can compare this pool to the overall

13South African population as a validation check on the efficacy of ICT recruitment with respect torepresenattiveness.
Table 2 begins with a comparison of the overall platform average across all channels; here we
see that while the age and gender of users closely match the national average, we have dramaticallyunder-recruited White users. While 8.9% of the national population is White, only 1% of platform
users who enter demographics self-identify as White. Because the census data is from 2011, despite
our close match to the national average age, given the very young population of the country wehave a dramatically smaller share of the platform who report having voted in the 2009 election;
38.5% on the platform versus 77.3% in the census.
In the lower panel of this table we break out the demographics by channel, and show thatdifferent technologies generate sharply distinct user groups. While the population is just less thanhalf female, two thirds of the USSD users in all arms were women. In contrast, the English andZulu-language social media channels skew male, at over 60%. Most striking is the dominance ofBlack users on the USSD channels; roughly 94% for every USSD arm. The language chosen on
the social media platform conforms in the way we would expect to the underlying ethnic groups,
with the Zulu-language version being overwhelmingly Black (96%), Afrikaans being dominated by
Coloured (72%) and White (11%) users, and the English-language version (which was the default)
being the most diverse. The confirmation that language proxies for demographics in the expected
way is useful because the language choice is observed for all social media users (even those who did
not answer the demographic questions); consequently in most of what is to follow we break out thethree languages separately in our analysis of the social media channel. Twitter is the only channel
with meaningful levels of Asian participation (8%). The final column gives voting rates in the priorelection; however these are not directly comparable in that the only available population data is
turnout amount registered voters, while on our platform we cannot distinguish those registered in
the prior election. Given an average age of 24 on our platform in 2014, it is not surprising that
only 39% had voted in the prior election.
These results, in combination with a multi-channel outreach strategy that is otherwise comparable across devices, imply that we must be very careful to use terms such as ‘ICT-driven participation’in any simple way. The technology interface on the user side is critical; the USSD platforms provide
meaningful outreach to groups otherwise marginalized in South African politics; fully 60% of theusers on this channel are Black women. As we move towards technologies that require smart phones(South African social media networks or Twitter) the demographics of users become dramaticallymore male and less Black. The implication is that the device gateway required to access ICT
platforms plays a very fundamental role in driving the pool of participants.

14

3.3

Crowdsourcing Data through the Platform

What do we actually learn about South African politics from the nature of the responses on theplatform? A natural starting point is to look at opinion polling, since this is a unique opportunity
to observe the evolution of support for the different political parties in near real-time in the weeks
leading up to the election. Figure 2 plots the results of all questions asked on the platform about
support for political parties across platforms and days, comparing them to the actual outcome ofthe election (a 62.1% vote share for the ANC). In large part these channel-specific polling results
flow from the demographic differences we have shown across the channels; the strong overall Blacksupport for the ANC manifests itself in all Black-dominated channels (USSD and Zulu social media),
while the pro-DA tilt of the Coloured and White communities is clear in the results for Afrikaanssocial media. Interestingly, the English social media channel, which is our most demographicallydiverse, closely resembles the national vote total on election day (60%). The channels that are notconsistently pro-ANC all see a strong swing towards support of the ANC in the 10 days prior to
the election. Critically for the issue of representativeness, because we have channels that err on
both sides of the correct vote total, given a nationally representative pre-election poll it would bepossible to weight our channels to give the correct national average while still being able to exploit
the temporal granularity of our data.10
A different advantage of mass-scale crowdsourcing can be seen in the spatial breadth of the
responses we recover. Presenting participation in terms of the enormous rates of non-compliancerelative to the initial sample illustrates a glass half-full; alternatively we can ask about the numberof places in which we are able to recover survey responses. Figure ?? presents a map illustrating
the number of different Phase 2 responses across South Africa. As we will discuss further in our
analysis of election monitoring, this suggests that crowdsourced data may be particularly usefulwhen we desire information on geographically dispersed events, but do not need more than one
reporter in each location. To the extent that the composition and the durability of the reporter isimportant then ICT techniques are at a disadvantage; if we seek a single reporter in many locations
to provide simple objective information, then crowdsourcing becomes increasingly attractive.
We provide a detailed look at the actual answers from the various surveys of political activity
in Tables ??, ??, ??, ??, and ??. For all these tables, the three USSD experimental arms serve as
the omitted category, so the coefficients on USSD Non-Experimental and the social media channels
give the observational differences in response rates (first column) and actual responses (subsequentcolumns) across technology channels.11 We provide a detailed analysis of the experimental difference
10

While overall average changes across time blend compositional changes with actual shifts in support, the extensive
margin effects can be limited by using only the sample of users who respond each time queried to calculate the weights.
11
For outcomes where there are sufficient responses over Twitter/Gtalk to include this channel in the regression results

15

in response rates across treatment arms in subsequent sections.
Several features of these tables merit particular mention. First, as we will discuss in the subsequent section, there are strong differential response patterns across channels, with those navigating
through a web interface the most likely to engage with passive content that must be navigated to,and mobile phone users most likely to respond to push messages that are sent out by text. Secondly,
the levels of political engagement, trust in the national government and the ANC, and satisfaction
with South African democracy are highest on the Zulu social media channel and lowest among the
Afrikaans users. Similar patterns hold in terms of opposition to the use of violence in politics, but
Zulu users are also much more likely to have reported personally engaging in non-violent politicalprotest. English social media users report lower levels of voter intimidation, vote buying, political intimidation, and protests, and are substantially less supportive of the justifications for using
violence, but nonetheless report very low levels of satisfaction with democracy and lower levels of
support for both political parties. In all our platform recovers 1,021 reports of political violence
and 961 cases of politicians buying votes with food in the run-up to the election, and 1,737 reported cases of voter intimidation on election day. While we did not design the platform to enablereal-time reporting of these events to the Independent Electoral Commission, our results suggestthat ICT-based crowdsourcing platforms can be a meaningful way of uncovering information about
voter preferences, politician behavior, and electoral irregularities at a national scale.

3.4

Observational Analysis of Participation

We now focus on participation itself as the key outcome of the study. Figure 3 shows the participation rate across different forms of engagement, with the y-axis in a log scale and participation
defined as the fraction of those who answered the initial engagement question who subsequently
participate in each type of activity. While registration rates are similar across channels, when we
focus on the Phase 2 digital forms of engagement we see that participation remains substantiallyhigher among the users of social media channels with their greater ease of interacting with digitalcontent on a smartphone interface. However, once we move to asking individuals to volunteer toserve as election monitors (an activity which entails substantially higher costs and whose costs are
no longer differentiated by channel), participation rates are now substantially higher for the USSD
users (5.1% for USSD, 3.5% for social media).
A second way of addressing this point is to use the response to the initial engagement question,
asked just before registration, to measure heterogeneity in levels of intrinsic motivation.12 Table
we do so.
12This question asked “It’s election time! Do u think ur vote matters?” Response options included, “YES, every votematters,” “NO, but I’ll vote anyway,” “NO, so I’m NOT voting,” “I’m NOT REGISTERED to vote,” and “I’m TOO

16

?? illustrates in a different way that users of higher-cost technological channels indeed have ahigher average level of intrinsic engagement in the political process. The most arduous path toenter our platform is the USSD non-experimental arm, who did not click through a PCM but
manually contacted the system and enrolled. This group has the highest intrinsic engagement,with 84% reporting that ‘every vote matters’. The Experimental USSD arms, that enrolled usinga click-through PCM but had to interact with the system on USSD, have the next-highest levelsof engagement. Users of the social media channels, which lowered the cost of participation, havethe lowest levels of engagement. Interestingly, engagement is lowest among users of the Zulu social
media channel, whose demographic makeup is most similar to the USSD channels.

3.5

Experimental Analysis of Participation

Table ?? provides estimates of the treatment effects from the initial PCM randomization.13 To
conduct a statistical analysis, we inflate the dataset to contain the correct number of millions of
PCMs sent by arm, with the observed success rates by arm included. This generates a data structurethat has the correct take-up rate per PCM sent, and permits a statistical test of significance acrossarms. As would be expected given the enormous number of observations these tests have highstatistical power; even the tiny -.002 percentage point difference between the Free and Standard
arms is significant at the 5% level, and the more than doubling of response generated by the
Lottery incentive is strongly significant. The PCM experiment generates two initial take-aways.
First, mobile airtime charges do not serve as a significant barrier to registration on the system.
Second, more than half of those who entered in the Lottery arm can be thought of extrinsicallymotivated in the sense that they would not have participated had they not been offered the lottery
incentive.
Next we turn to the analysis of responses to the polling questions that were pushed out during
Phase 2. For these questions, USSD users retained the treatment status they were assigned at entry(none, Free, or Lottery) for all questions answered, and social media users were never incentivized.
We further randomized the sequence of questions, and the day on which questions were asked.
Table 3 presents an analysis of these overlapping experiments. Column 1 presents the most basic
experimental comparison using only the USSD sample, and shows that there is a similarly higher
response rate for both the Free and Lottery arms (who do not pay for each response), suggestingthat the cost of interaction begins to become salient once interaction is repeated. Nonetheless, thiseffect is small, at just over half of a percentage point or 10% of the control group response rate.
YOUNG to vote.”
13Note that we are not able to conduct any type of balance tests on the PCM randomization because we have no
information on the numbers to which these were sent or the characteristics of those individuals.

17

All of the social media channels display lower rates of responding to these push questions, despite
the fact that we restrict this analysis to the subset of users who volunteered phone numbers andhence to whom we were able to push questions via SMS. The implication is that text is not a good
way to interact with people who initiated contact online. The depression in social media response
rates is, however, much smaller for Zulu and Afrikaans users than it is for English users.
Moving to Column 3 we see that the response rate is similar on weekdays and weekends, andthat while the second question has similar response rates to the first, by the third question there isa significant drop, and by the fourth and fifth the drop is almost 1.5 percentage points. So there
is a fatigue effect. Dummies for the question type are insignificant, suggesting that individuals are
not deterred from responding by the nature of the question being asked. Column 4 re-runs theregression in Column 3 on the sample for whom we have demographic data, and Column 5 thenincludes the demeaned demographics as controls (but does not report coefficients). Inclusion of these
demeaned demographic variables removes the differences across channels on observed attributes,
and gives the differential treatment effects for a regression-adjusted average age, sex, race, and 2009
voting probability. Given that the incentive treatments influenced the probability of answering thedemographic variables, these results should be treated with caution as they involve including a ‘bad
control’. They do however give a sense of the extent to which observable demographic differences
drive the response differentials across channels. The sample that reported demographics displays
less of a difference across channel than the full sample, the invariance to the inclusion of thedemographic controls suggests that cross-channel differences are not driven by observably different
user composition across channels.
Next we turn to the analysis of the experiment that randomly incentivized users to volunteer
as citizen election monitors. Table ?? shows that the monitoring invitations were well balanced
over variables observed at the time of the experiment. Of those invited to monitor, 2,507 agreed tovolunteer, signed the new TCs, and provided all relevant location information required to identify
their polling place. We were thus able, through the platform alone, to recruit citizens willing toobserve 12% of the polling stations in 37% of the wards in the country.
Column 1 of Table 4 shows that 5.1% of those asked to volunteer without incentives did so, andthe incentive pushed the rate to 6.7%, with the difference strongly significant. So even when wemove to this higher-cost form of action, incentives provided through ICT continue to be effective.
Once in Column 2 we break out the (experimental) USSD arms and the (observational) social media
arms, however, we see a more complex pattern. The Free arm volunteers at much higher rates,suggesting that USSD costs may be a barrier to mobile phone users being willing to navigate thesystem to volunteer. The overall rate of volunteering in the social media arm is lower, but this effect18

is restricted to the default English user group, with Zulu and Afrikaans users volunteering at similar
rates as the USSD Standard control group. More surprisingly, we find that the largely extrinsicallymotivated Lottery arm of the USSD and the largely intrinsically motivated USSD Non-experimentalgroup volunteer at similar rates as Standard, and the Free arm is actually more likely to volunteer.
In Column 3 we estimate the same regression on the sub-sample for whom demographic data are
available, and in Column 4 include the demographic determinants of volunteering. The (unreported)
results on demographics suggest that older individuals and those who voted in the prior election
are much more likely to volunteer, and White and Coloured respondents are weakly less likely to
do so. Nonetheless actually controlling for those demeaned variables makes almost no difference
to the coefficients, suggesting that the differences across platforms are not arising from observable
demographic differences. Hence we find current incentives to have the expected effect, but therelationship between prior incentives and volunteering is more complex than expected. We returnto this relationship in the subsequent section.

3.6

The Dynamics of Extrinsic Motivation

Turning to the dynamic impact of incentives, we expect the marginal effect of incentives will
be stronger in the group recruited through external incentives because this group includes more
extrinsically motivated individuals. To test this, we employ a difference in differences design: theeffect of incentivization should be larger for those who have already shown sensitivity to incentives.
We exploit the fact that the Demographics questions in Phase 2 were incentivized via lottery for allparticipants, while the ‘What’s Up’ questions were not incentivized for any participants. We canlook at the differential response rates to these two sets of questions for initially incentivized (Freeand Lottery) and un-incentivized (Standard) groups to understand how recruitment incentives alter
the differential efficacy of subsequent incentives. We expect that the differential participation rate
between incentivized and un-incentivized questions will be larger in the group that was recruitedusing extrinsic incentives than the group that was not. Because the lottery treatment was the mosteffective at inducing participation, we are particularly interested in differential effects in this arm as
being unequivocally composed of more extrinsically motivated individuals than the standard arm.
Column (1) of Table 5 shows the Free and Lottery groups are about 8 percentage points more
likely than the Standard group to answer incentivized questions. Column (2) shows that thedifference in the willingness to answer un-incentivized questions is either zero or very small relative
to Standard. Consequently, when in Column (3) we show the difference in differences between
incentivized and un-incentivized questions, both incentive treatments result in differential response
rates on the order of 6.7 points (Free) to 8.4 points (Lottery). Thus, the drivers of response rates

19

to crowd-sourced data collection include not only contemporaneous incentives, but the history of
incentives that has shaped that user group over time. In this sense our evidence is doubly positiveon the use of enrollment incentives (higher overall subsequent participation plus higher subsequent
responsiveness to extrinsic incentives). As above, in Column (4) we first restrict the sample tothose with demographics, and then in Column (5) control for demeaned demographics, and we findthat the best-motivated result (greater differential sensitivity for the highly extrinsically motivatedLottery group) survives these controls. Hence, those who were incentivized once respond moststrongly to subsequent incentives in their responsiveness to survey questions.
Table 6 then brings together all of these sources of variation into a single analysis of dynamics,
exploiting differences across channel, across initial subsidy status, and across extrinsic motivation
to study heterogeneity in the response to the monitoring incentive. Column (1) uses only theexperimental USSD sample, and finds that the differential effect of these small initial incentives
disappears as individuals are asked to engage in real-world activities that bear much larger costs
than the magnitude of the randomized incentives. That is, once the cost of the action exceeded thedifferential costs across channels and across incentives, the differential participation probabilities
generated in the recruitment process are no longer important in determining who engages in thereal-world. The logic for this apparently surprising result is that while incentives for costly formsof participation are effective across all channels, they are not differentially so across the extensive
margin variation generated by the PCM experiment because none of those who were induced toregister by small PCM incentives will engage in the high-cost activity anyway.
In Column (2), we incorporate the role of intrinsic motivation (the dummy for reporting ‘every
vote matters’ in the initial Engagement question), and its interaction with extrinsic incentives tovolunteer. Here we see that not only does extrinsic motivation strongly predict the willingness
to volunteer, but that extrinsic incentives actually have a stronger effect among the intrinsicallymotivated. Indeed, by looking at the first row in this column we see that incentives have only a weakeffect among those who don’t report being intrinsically motivated. Consistent with this logic, whenin Column (3) we examine the Social Media users (who have lower overall engagement, as shown
previously), we see that while there is still some role of intrinsic motivation in driving volunteering,
extrinsic incentives have no effect, either alone or in interaction with intrinsic motivation. So, farfrom finding a crowd-out, it appears that a kind of complementarity exists between the intrinsic
desire to engage with political action and the responsiveness to extrinsic motivation. In Column(4) we then pool the USSD Experimental sample with the Social Media users, and formally test forthe difference in the differential effect of incentives for the intrinsically engaged across USSD andSocial Media. Here we again find evidence of complementarity in that extrinsic incentivization is20

significantly less effective on the channel that has lower levels of intrinsic motivation (social media).
Comparison across columns (5) and (6) again suggest that observable demographic differences acrosschannels are not driving our results.
Some dimensions of these results are strongly intuitive: incentives matter, intrinsic motivation
matters, and the USSD channel that imposed the highest user cost has both higher overall intrinsic
motivation and higher willingness to participate in costly political action. Some dimensions are
more surprising; the Lottery arm, which should contain the most extrinsically motivated individuals
among the USSD users, is not the most responsive to subsequent incentives, as we would expect.
Instead it is the Free arm, which did not appear to have induced additional participation in thefirst place, that sees the greatest volunteering. One explanation for this result is that while costof interacting with the platform is not an inhibitor to initial registration, over the dynamic period
of interaction required to volunteer several weeks later, USSD usage costs do become important towillingness to continue to engage. It is also surprising prima facie that incentives are so much lesseffective on the social media channels where overall motivation is lower. However, once we recognize
the complementarity between extrinsic and intrinsic motivation that runs through the results inTable 6, it appears to square many of these findings. For user groups like the USSD Lottery arm andthe social media users where we might otherwise expect intrinsic motivation to be low and hence
extrinsic incentives to be important are dampened by their lack of complementarities, leaving thestrongly intrinsically motivated groups like USSD Standard and Free as the ones most responsive
to incentives. The lack of crowd-out, and indeed direct complementarity, between incentives toparticipate is an important policy result, suggesting that efforts to promote participation throughextrinsic means will in fact reinforce underlying desires to engage.

4

Citizen Election Monitoring

4.1

Incentives and Monitoring Quality

We now turn our attention to the actual monitoring of polling places conducted by platform participants on election day. As described in Section 3, the actual invitations to monitor were subjected
to a data error, and were almost exclusively confined to individuals in the USSD Standard arm.
Nonetheless, the data let us recover a well-defined control group (invited individuals were either
assigned the low incentive of R5 or the high incentive of R55), and the individuals assigned tothe high-powered incentive within this group appear to be random. Table ?? examines a set ofpre-defined characteristics, including Phase 2 response rates, whether they were included in theoriginal volunteer sample, and then their responses to the engagement and demographic questions,21

and finds no significant differences between the high- and low-incentive groups. Nonetheless, thiscomparison is not randomized in a controlled way and we therefore consider this component of thestudy to be quasi-experimental.
Invitations and instructions to monitor were sent to 1,830 individuals (who had not previously
volunteered to be CO volunteers) asking them to observe the voting tallies the day after the election;of these unwitting volunteers, 322 submitted information via SMS about their polling stations. The
invitation to monitor was comprised of several elements. Those who had smartphones were invited
to photograph the tally of the polling precinct results which must by South African law be posted
at the end of voting. Figure ?? provides an example of a photograph provided by a VIP:Voice user.
For those without smartphones, they could use the platform to record the information on the sheet,providing the precinct number and the vote totals for each party via SMS. As shown before, thegeographic coverage that we achieved from the platform was impressive; Figure ?? shows a mapof the country and Figure ?? a map of the Durban area, illustrating the wards in which we had acitizen observer provided a report.
The results of the invitations to monitor are presented in Table 7. Incentives are similarly
effective in the CO volunteer experiment in Phase 3. When offered payment of R5, only 17.6% of
those deployed to observe entered any data on their polling places. In contrast, among those offered
the more substantial payment of R55, this rate jumped to 27.4%. Unfortunately the data qualityof the SMS-based tallies was poor; a large majority of the users who provided this information did
not do so in a manner that would have generated machine-readable data formats on the vote totals
along with a recognizable precinct number. Within the sample that observed, the rate of successful
entry of ANC voting data via SMS more than doubled, from 2% to 4.7% for those offered the larger
incentive.14 Hence while incentives are effective at improving both the response rates and the data
quality, it is clear that quality control and lack of training are in practice a constraint on the abilityof this type of crowd-sourced approach to provide a meaningful check on electoral malfeasance.
Nonetheless, we successfully garnered a set of volunteers to serve as election monitors. We received data from actual observers, who returned to polling stations on the day after the election toobserve whether or not a declaration of results form (tally sheet) had been posted, submitting information about the tally via SMS, and taking a photo of the tally if equipped with a camera-enabledphone. The results from citizen and actual volunteering shows us that with no training or previous
contact other than through the platform, we were able to generate good polling station coverage.
This suggests potentially powerful form of citizen-based monitoring, and could achieve monitoring
14

We do not control for demographics in this table because of data limitations. However, 100% of COs who provided
demographic data were Black. We also do not control for entry strata as virtually all of the actual observers came fromthe standard USSD treatment group.

22

at scale. We therefore proceed to conduct a cost-effectiveness analysis of this form of unstructured,
zero-training citizen observation as compared to several other modalities for monitoring pollingplaces.

4.2

Comparative Cost EffectivenessWe can benchmark crowd-sourcing as a means to achieve successful monitoring relative to other,more direct techniques. To do so, we can use cost per successfully monitored polling station toyardstick our citizen crowd-sourcing approach relative to standard and well-documented alternatives
for election monitoring.15 The rows of Table 8 compare potential ways of scaling our platform
nationally. Based on the results presented here, we simulate costs for three variants of platform:
where all users are unincentivized, where all users are incentivized both in enrollment and inmonitoring (thus creating the feedback loop described above), and a counterfactual scenario where
all platform users who volunteered to monitor actually did so. To broaden the comparisons, we
also provide costs from a parallel field exercise where we worked with a local organization to hire
and train South African monitors to observe stations using pen-and-paper techniques, and from
Callen et al. (2016) with locally trained and paid monitors using a mobile-phone application andinternational election monitoring costs in Uganda.
The first column in Table 8 shows the recruitment and training cost per user, where we amortize the platform development costs over the number of monitors that would have materialized
had we run the entire national platform under each of these scenarios.16 The second column gives
the number of stations to be monitored per individual, the third column the rate at which successful monitoring occurs, and the fourth column gives the final cost per station monitored. Low
overall participation on an unincentivized platform makes the cost per station very high at morethan $7,000. Incentives improve participation enough to more than pay for themselves in costeffectiveness terms, driving the cost per station to about $5,400. However, under a counterfactual
scenario in which all those who volunteered to monitor successfully did so, costs per user would fallto just $166, slightly higher than the figure for our locally trained professional monitors ($101) orsimilar local monitors employed in Uganda per Callen et al. 2016 at $40 (but significantly cheaper
than international monitors in Uganda at $6,200).
15

It is worth noting that our platform had other purposes, and crowdsourced information on many more outcomesthan polling results alone, and so in this sense the cost-effectiveness comparison is conservative in our case.
16
We calculate a cost per invited observer on our platform by multiplying the number of volunteer citizen observers(2,507) times the rate at which those whom we invited to monitor did so (12% and 22% for unincentivized and incentivized
observers, respectively) to calculate the number of citizens who would have agreed to monitor if all who had volunteered
had been invited. We then apportion the total VIP:Voice costs ($420,507), which aggregates platform development($383,256) and media costs ($37,251), to these citizen monitors’ share of the total cost in column (1), and include thecost of the incentive ($4) in the second row.

23

The promise of crowd-sourcing to achieve mass coverage is seen in the last column of Table A-4,
which shows a marginal cost per additional station of only $15 once the VIP: Voice platform fixed
costs are sunk, 85% lower than the marginal cost of professional monitors. Overall, the crowdsourcing approach is not cost-effective with only a few monitored stations, but with viral adoption
and as coverage of stations grows, citizen-based ICT observation achieves cost effectiveness relative
to a similar scale of professional work either by local or international monitors.
We read these results as suggesting that platforms such as VIP:Voice are best thought of as a way
to supplement (rather than replace) structured user bases from non-governmental and internationalorganizations. One of the important promises of crowd-sourced, ICT-based monitoring of elections
is its potential to achieve relatively inexpensive and broad national coverage through scaling andviral adoption. Citizens have the potential to provide eyes and ears in every polling station in all
corners of a country. Nonetheless, it is clear that this type of unstructured data feedback suffersfrom quality issues, and the presence of international election observers may inherently serve as acheck on electoral corruption in a way that domestic citizen observation is unable to replicate.

5Conclusion

This paper presents the results from VIP:Voice, a nationally-scaled ICT election platform built to
recruit citizens across a variety of digital channels in order to encourage political participation indemocracy-enhancing activities. Our study examines how ICT recruitment interacts with digital
participation and engagement in real-world political activity. While recent research finds ICTinterventions can increase political participation, our study is unique in providing comparable accessacross multiple technological channels, along with a rich set of randomized extrinsic incentives toparticipate. Understanding the intersection between channel costs, incentives, and the types ofpolitical engagement that can be engendered is critical for a better understanding of how ICT canbe used to promote beneficial forms of political engagement.
Despite impressive overall recruitment in VIP:Voice, we find attrition across time and activities
forms a critical component to the story. ICT-generated crowds are both fragile and malleable.
Requiring users to bear the costs of digital interaction may not be important to initial engagement, but becomes increasingly important over the course of time as we seek to retain users andparticularly to engage them in more costly forms of political activity. Initial incentivization makesusers more malleable in the sense that they then respond more strongly to subsequent incentives
to enter crowdsourced information through the platform. Critically, we uncover a number of piecesof evidence that extrinsic incentives are actually complementary to intrinsic motivation to partici-

24

pate. Indeed, our study finds not only low willingness to engage in high-cost participation for those
who initially report being disengaged, but also that incentives are completely ineffective among
the disengaged and strongly effective among the engaged. Only the politically engaged respond to
extrinsic incentives. We believe that several factors may explain this surprising lack of crowd-out
in our study. First, few of the forms of political behavior we were inviting people to participate inwould have been externally observable, which may have limited the scope for the kinds of imagebased motivations that are discussed in Ariely, Bracha and Meier (2009). Second, the extrinsic
incentives were similarly not externally observable, limiting the extent to which they would inhibit
the appearance of ‘doing good’. Finally, the fact that incentives to serve as a Citizen Observer are
only effective for individuals who report being motivated about the election may simply indicatethat there is a threshold level of motivation below which individuals are too far from the participation constraint to be motivated by small monetary payments. In any case, this complementarity
is an important result of the study.
Our results also inform discussions within the ICT community about the implications of technology channel choice. Starkly different demographic profiles across channels suggests there is no
simple answer to the question “Can technology improve participation by under-represented groups?”
Rather, the relevant question is: “Which blend of technologies will yield the final user profile that
we want?” Ultimately, the transformative potential of ICT depends on how citizens use technology.
We show that with appropriate channel choice, an ICT approach can achieve outreach far beyond
the young, male demographic that dominate smartphone-based social media, broadening participation further using extrinsic incentives. Political engagement initiated in the digital realm, including
participation incentives delivered via ICT, do cross over to activity in the real world. ICT cantherefore play a central role increasing citizens’ participation and their contribution to the qualityof democracy.
Finally, our findings offer guidance on the practical possibility of using ICT platforms to promote
democracy-enhancing activities such as election monitoring. The strength of our crowd-sourced approach lies in its very broad geographic reach and low marginal costs of acquiring additional users.
The weakness is the difficulty of retaining users via this indirect type of interaction, and consequently issues with data quality as well as with guaranteeing the presence of a citizen reporter inany specific place. However, our results show that extrinsic incentives are not only strongly effective at driving participation, but actually enhance data quality as well (potentially, such incentives
should be based on submitting usable data to allow them to drive quality more directly). Thissuggests that strategically targeted incentives to users in specific locations, on top of a pre-existing
ICT platform of this type, may on the margin be a very cost-effective way to generate report-

25

ing on political behavior. While recent years have given social media a bad name in generating
political misinformation, this study illustrates the ways in which careful curation of information
feedback pathways can allow ICT to engender new forms of mass-scale political participation andprovide an effective check on malfeasance in the electoral process without becoming a conduit for
misinformation.

26

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Tables and Figures
Table 1: Waterfall of Study Participation

31
Notes: Table provides the waterfall of study participation, beginning from initial contact with the system and moving through the activities in the three phases ofthe project.

Table 2: Demographics by Channel

32

Table 3: Response rates to Push and Thermometer questions in Phase 2.
(1)
USSD
Experimental
0.008∗∗(0.003)(2)
Whole
Sample0.007∗∗(0.003)(3)
Questiontiming0.007∗∗(0.003)(4)
Samplewith demog-0.012(0.009)(5)
Controllingfor demog-0.013(0.009)0.006∗∗(0.003)0.006∗∗(0.003)0.006∗∗(0.003)-0.009(0.008)-0.012(0.008)USSD Non-Experimental

0.007∗∗(0.004)0.007∗∗(0.004)-0.020∗(0.010)-0.024∗∗(0.010)English Social Media-0.077∗∗∗(0.002)-0.078∗∗∗(0.002)-0.138∗∗∗(0.007)-0.131∗∗∗(0.007)

Afrikaans Social Media-0.002∗∗∗(0.001)-0.002∗∗∗(0.001)-0.005∗∗∗(0.002)-0.010∗∗∗(0.003)

Zulu Social Media-0.001∗∗(0.001)-0.001∗∗(0.001)-0.004∗∗∗(0.001)-0.006∗∗∗(0.001)

Weekday

0.001(0.001)-0.007∗∗∗(0.001)-0.007∗∗∗(0.001)

Second question-0.001(0.001)-0.004(0.003)-0.004(0.003)

Third question-0.003∗∗∗(0.001)-0.008∗∗∗(0.002)-0.009∗∗∗(0.002)

Fourth/Fifth question-0.014∗∗∗(0.001)-0.016∗∗∗(0.002)-0.016∗∗∗(0.002)

Question is Poll

0.001
(0.001)-0.002(0.002)-0.002(0.002)

Question is Rallies

0.001(0.001)0.005∗(0.003)0.005∗(0.003)

Question is Violence-0.001(0.001)

0.002(0.003)0.002(0.003)

Question is campaigning

0.001(0.001)364376
0.085
0.037

0.000
(0.003)105822
0.085
0.068

0.000
(0.003)1058220.085
0.071

USSD Free Arm

USSD Lottery Arm

Observations
Mean for USSD standard
R squared

1671210.085
0.000

3643760.085
0.036

Notes: Table provides the response rates to the Push and Thermometer polling questions, analyzed in a long panel
where the unit of analysis is the attempt to solicit each answer from each user. The USSD Standard arm is the omitted
category, so the first two rows estimate experimental differences for the randomly incentivized USSD groups, and theremaining rows are the observational differences across technology platforms. Column (1) contains only theexperimental sample while remaining columns use all available data. Column (4) is estimated only for the users who
provided demographic data, and Column (5) controls for (but does not report) demeaned demographic variables and sogives the impact on an average person, having removed observable demographic differences across channels. Robust
standard errors in parentheses, clustered at the level of the individual respondent. Each individual was asked to answer
five questions.

33

Table 4: Volunteering to Monitor.
(2)
MonitorVolunteered
0.016∗∗∗(0.002)(3)
Samplewith demog
0.016∗∗∗(0.004)(4)
Controllingfor demog
0.016∗∗∗(0.004)

USSD Free Arm

0.012∗∗∗(0.004)0.057∗∗∗(0.014)0.056∗∗∗(0.014)

USSD Lottery Arm-0.000(0.003)-0.013(0.011)-0.017(0.011)

USSD Non-Experimental

0.003(0.005)0.004(0.015)-0.003(0.015)English Social Media-0.014∗∗∗(0.004)-0.074∗∗∗(0.010)-0.065∗∗∗(0.010)

Zulu Social Media-0.005(0.004)-0.005(0.004)-0.007(0.004)

Afrikaans Social Media-0.007(0.006)-0.007(0.006)-0.001(0.008)

Twitter/Gtalk

0.101(0.062)
50814
0.051
0.003

0.040
(0.063)
18781
0.111
0.029

0.037
(0.064)
18781
0.111
0.035

Incentivized to monitor

Observations
Mean for USSD standard
R squared(1)
MonitorVolunteered
0.016∗∗∗(0.002)

50814
0.051
0.001

Notes: Table estimates the probability of volunteering to monitor in Phase 3 as a function of the arm and channel ofthe study, estimated only for the sample invited to monitor. The USSD Standard arm is the omitted category, so boththe ‘incentivized to monitor’ and Free/Lottery comparisons are experimental, while the remaining differences in thetable are observational. Column (1) presents the simple experimental analysis, while Column (2) includes
channel-specific fixed effects. Column (3) is estimated only for the users who provided demographic data, and Column(4) controls for (but does not report) demeaned demographics and so gives the impact on an average person, having
removed observable demographic differences across channels. Robust standard errors in parentheses.

34

Table 5: Incentivized versus Unincentivized Phase 2 Responses.

USSD Free Arm

USSD Lottery Arm(1)
Answered
Incentivized
0.079***(0.007)(2)
Answered
Unincentivized
0.019***(0.004)(3)
Diff Prob(Inc - Uninc)
0.067***(0.007)(4)
Samplewith demog-0.016(0.010)(5)
Controllingfor demog-0.017*(0.010)0.082***(0.006)-0.003(0.003)0.084***(0.005)0.031***(0.008)0.026***(0.008)Age

0.003***(0.000)Male-0.022***(0.006)

Coloured-0.064***(0.016)White-0.066**(0.031)

Asian-0.022(0.039)Voted 2009

0.012**(0.006)
8579
0.057
0.019
0.000

Observations
Mean for USSD Standard
R squared
Ftest Free vs Lottery

40339
0.067
0.005
0.562

40339
0.018
0.001
0.000

40339
0.057
0.005
0.002

8579
0.057
0.006
0.000

Notes: Table uses only the experimental USSD sample. Column (1) analyzes the probability of any responses to Phase
2 questions that were incentivized, and Column (2) the probability of any responses to Phase 2 questions that users
were not incentivized to answer. Column (3) takes as the dependent variable the difference between these two dummies(Incentivized - Unincentivized). column (4) repeats this analysis for the users who provided demographic data, andColumn (5) controls for (demeaned) demographics and so gives the impact on an average person, having removed
observable demographic differences across channels. Robust standard errors in parentheses.

35

Table 6: The Dynamics of Extrinsic and Intrinsic Motivation.
(1)
USSD
Only
0.023∗∗∗(0.006)

(2)
USSD
Only
0.015∗∗(0.006)Pooled
0.013∗∗(0.006)(5)
Samplewith demog-0.017(0.020)(6)
Controllingfor demog-0.015(0.020)

USSD Free Arm

0.019∗∗∗(0.005)0.018∗∗∗(0.005)0.018∗∗∗(0.005)

0.061∗∗∗(0.019)0.061∗∗∗(0.019)USSD Lottery Arm-0.001(0.004)-0.005(0.004)-0.005(0.004)-0.026∗(0.014)-0.029∗∗(0.014)

Free*Incentive-0.013(0.008)-0.013(0.008)-0.013(0.008)-0.009(0.028)-0.012(0.028)Lottery*Incentive

0.000(0.007)-0.002(0.007)-0.002(0.007)0.011(0.021)0.010(0.021)

Incentivized to monitor

(3)
SM
Only
0.002
(0.005)(4)

Engaged0.026∗∗∗(0.003)0.017∗∗∗(0.005)0.023∗∗∗(0.003)0.022∗∗∗(0.005)0.015∗∗∗(0.005)

Incentive*Engaged

0.012∗∗(0.005)0.000(0.007)0.015∗∗∗(0.005)0.062∗∗∗(0.013)0.061∗∗∗(0.013)

SM*Incentive-0.007(0.007)0.022(0.021)0.019(0.021)Social Media user

-0.004(0.004)-0.061∗∗∗(0.013)-0.053∗∗∗(0.013)

SM*Incentive*Engaged-0.021∗∗∗(0.006)
46899
0.051
0.007

-0.067∗∗∗(0.013)
18045
0.111
0.035

-0.066∗∗∗(0.013)
18045
0.111
0.040

Observations
Mean for base group
R squared

35379
0.051
0.003

35379
0.051
0.007

11528
0.035
0.002

Notes: Table analyzes the probability of volunteering to serve as a Citizen Election Monitor, conditional on having
been invited. Columns (1) and (2) use only the experimental USSD sample, (3) only the Social Media channels, andthe remainder pool these two together. Column (5) is estimated only for the users who provided demographic data, andColumn (6) controls for (demeaned) demographics and so gives the impact on an average person, having removed
observable demographic differences across channels. ‘Engaged’ is a dummy for answering ‘Yes every vote matters’ tothe initial Engagement question asked at Registration. The Incentive is a dummy for receiving a financial incentive of
5R to serve as a Monitor. Robust standard errors in parentheses.

36

Table 7: Actually Performing Monitoring.

High Incentives
Observations
Mean for Low Incentive
R squared(1)
Monitoring
Performed
0.098∗∗∗(0.017)
1830
0.176
0.016

(2)
Entered Usable
Data, whole sample
0.027∗∗∗(0.006)
1830
0.020
0.009

(3)
Entered Usable
Data, among respondents
0.104∗∗∗(0.031)
322
0.116
0.022

Notes: Table analyzes an outcome for actually performing monitoring (Column (1)), and entering usable monitoringdata (Columns (2) and (3)), with the final column estimated only among those who entered any monitoring data. The
dummy ‘High incentive’ is an indicator for having been offered the 50R (rather than 5R) incentive to perform
monitoring. This treatment was not directly randomized. Robust standard errors in parentheses.

37

Table 8: Comparative Cost EffectivenessMonitoring Modality

Recruitment &Number of Stations

Fraction of Stations

Cost per Effectively

Marginal Cost

Training Cost per

per Monitor

Effectively Monitored

Monitored Stationof Additional

Monitor

StationSouth Africa:
Citizen Monitors, unincentivized$1,381.50

1

4.2%$7,635.42

Citizen Monitors, incentivized$764.461

14.2%$5,383.52

Citizen Volunteers, if perfect compliance rate

$165.781

100.0%$165.78$14.69ICT-enabled Local Monitors$293.99

3.3

88.0%

$101.24$101.24

38

Uganda (Callen et al. 2015):
Traditional Professional Monitors$6,220.00

CT-enabled Local Monitors$40.00Note: the ‘South Africa’ results provide cost estimates from our own intervention. The cost of a Citizen Volunteer is calculated by multiplying the number ofvolunteer monitors times the rate at which those we invited to monitor to get the number of successful citizen monitors we would have yielded if all were invitedunder each treatment status. We then apportion the total platform development costs ($420,507) to these citizen monitors’ share of the development cost to getcolumn (1). The third row is a counterfactual exercise assuming that every volunteer citizen monitor was able to effectively monitor the station. The fourth row
uses numbers from a parallel experiment in which we worked with a non-governmental organization to train local college students to use the platform for election
monitoring (these monitors were paid and had expenses reimbursed). The Uganda results are from Callen et al. (2016), and report the costs for international
monitors based on the European Union Election Observation Mission to the country, and for ICT-enabled local recruits are based on the use of a simpler, featurephone-based election monitoring system.

Figure 1: Study Design39Figure 2: Daily Opinion Polling40

Figure 3: Participation Rates by Activity and Channel

41