Working Paper SeriesNumber 72

Southern Africa Labour and Development Research Unit

byJustine Burns and Malcolm Keswell

Ethnolinguistic Diversit y and the Provision of PublicGoods: Experimental Evidence from South Africa

About the Author(s) and Acknowledgments

Justine Burns: School of Economics, University of Cape Town, Rondebosch 7700, Cape Town email: justine.burns@uct.ac.za

Malcolm Keswell: Southern Africa Labour and Development Research Unit &School of Economics, University of Cape Town, Rondebosch 7700, Cape Town email: malcolm.keswell@uct.ac.za

We would like to thank Brandon Trew for excellent research assistance, and Susan Godlontonfor additional assistance in running the experiments. This work was funded by a grant from theOrganisation for Social Science Research in Eastern and Southern Africa (OSSREA). Comments arewelcome.

Recommended citation

Burns, B. and Keswell, M. (2011). Ethnolinguistic Diversity and the Provision of PublicGoods: Experimental Evidence from South Africa. A Southern Africa Labour and Development Research Unit Working Paper Number 72. Cape Town: SALDRU, University of Cape Town

ISBN: 978-1-920517-13-7

© Southern Africa Labour and Development Research Unit, UCT, 2011

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Ethnolinguistic Diversity and the Provision of PublicGoods: Experimental Evidence from South Africa

Justine Burns and Malcolm Keswell∗

Draft: September 2011Abstract

This paper utilises techniques in experimental economics to investigate theimpact of racial identity on the provision of public goods. A large sample ofBlack and White undergraduate University students were recruited to participatein public goods games, where the racial composition of the groups was varied toinclude All White groups, All Black groups and mixed race groups (comprisingBlack and White students). The results show that contrary to predictions fromsocial identity theory, racial homogeneity in a group does not uniformly predicthigher contributions to the public pool. Rather, it would appear that observableracial identity may convey information about extensive heterogeneity as opposedto homogeneity, especially where race is highly correlated with diversity in otherdimensions, such as ethnolinguistic diversity. In accordance with the establishedmacroeconometric literature on the provision of public goods, the results presentedin this study show that contributions to the public good are indeed increasing inthe level of trust in a group, and declining in the extent of ethnolinguistic diversityand socio-economic need in the group. Moreover, while communication has a largeand significant effect on contributions to the public pool, patterns of communi-cation are affected by the racial composition of the group, with Black studentsappearing to be more responsive to communications made by White colleagues asopposed to Black colleagues. Hence, communication is not effective at sustainingco-operation in racially homogenous Black groups, possibly because communica-tion in these groups allows participants to verify the greater diversity on otherdimensions amongst group members.

JEL Keywords: Public goods, experiments, ethnolinguistic diversity, co-operation

JEL Codes:

∗We would like to thank Brandon Trew for excellent research assistance, and Susan Godlontonfor additional assistance in running the experiments. This work was funded by a grant from theOrganisation for Social Science Research in Eastern and Southern Africa (OSSREA). Comments arewelcome. Please direct them to justine.burns@uct.ac.za and malcolm.keswell@uct.ac.za

1 Introduction

Economists have become increasingly interested in the role played by ethnic heterogene-ity in explaining variation in cross-country growth regressions, with some attributingthe lack of economic growth in many African countries to this phenomenon. (Easterlyand Levine, 1997; Englebert, 1997; Collier, 1998, Rodrik, 1999; Collier and Gunning,1999; Masters and MacMillan, 1999.) While empirical estimates of the association be-tween increased ethnic diversity and economic growth range from 2-3% (Masters andMacMillan, 1999; Easterly and Levine, 1997; Collier, 1999a), there is less agreement onthe mechanism through which ethnic diversity affects economic growth.

One school of thought argues that heterogeneity results in the under-provision ofpublic goods, since heterogeneity undermines group cohesion, thereby raising the trans-actions costs of bargaining. Individuals may be more prone to co-operate when others intheir group or community are similar to them, since this fosters a strong group identity(Kramer and Brewer, 1984; Kollock, 1998). Groups characterised by greater hetero-geneity, be it extreme wealth inequalities or ethnic diversity, may be less successful inresolving collective action dilemmas, not only because polarised societies may be moreprone to competitive rent-seeking by different groups within that society, but also be-cause such diversity may promote polarisation in preferences, thereby making it difficultto reach consensus of the type and quality of public goods and services to be provided(Baland and Platteau 1997a,b; Bardhan et al, 1998; Dayton-Johnson and Bardhan,2002;, Persson and Tabellini, 1994; Alesina and Tabelini, 1989; Alesina and Drazen,1991; Schelifer and Vishy, 1993; Alesina and Rodrik, 1994; Alesina and Spoloare, 1994,Lane and Tornell, 1995, Easterly and Levine, 1997).

There is also increasing evidence that the channel through which heterogeneity andwealth inequalities affect co-operation in the provision of public goods is through itsimpact on social capital, trust in particular. To the extent that similarities in wealth,ethnic or racial attributes are used as ”information shortcuts” concerning the reliability,shared values and expectations of participants in an exchange, homogeneity may reducetransactions costs, thereby raising social capital or trust, and increasing the likelihood ofco-operative behaviour in resolving social dilemmas. (Knack and Keefer, 1997, Alesinaand LaFerrara, 2000, Bardhan 1993, 2000; Dayton-Johnson, 1997,Varughese and Os-trom, 2000; Messick and Brewer, 1983, Coleman, 1990) However, in segmented societies,to the extent that trust is correlated with group affiliation and inversely related to the so-cial distance between groups, greater heterogeneity erodes the potential for co-operativebehaviour across groups (Zak and Knack, 2001; Bouckaert and Dhaene, 2003; Akerlof,1997).

An alternative school of thought, however, posits that heterogeneity will result inhigher provision of the public good since heterogeneity is associated with a less well-endowed median voter, who ”votes” in favour of public good provision. Moreover, if thebenefits of public goods are purely localised, and enjoyed by specific groups alone, thena common pool model may well imply the over-provision of public goods in the contextof ethnic or income diversity (Alesina and Drazen, 1991).

This paper adopts an experimental approach in examining the impact of hetero-

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geneity, specifically racial heterogeneity, on the provision of public goods. As such, itprovides an experimental test of this larger empirical literature. We report the resultsfrom a series of public goods games in which the racial composition of the group wasvaried, in order to examine whether there is, in fact, less co-operation in racially het-erogeneous groups as opposed to racially homogenous groups. While ethnicity and raceare largely social and historical constructs, they remain powerful constructs in SouthAfrica, the setting for these experiments. Contrary to our expectations, we find thatracial homogeneity in a group does not uniformly predict higher contributions to thepublic pool. Rather, it would appear that observable racial identity may convey infor-mation about extensive heterogeneity as opposed to homogeneity, especially where raceis highly correlated with diversity in other dimensions, such as ethnolinguistic diversity.Moreover, while communication has a large and significant effect on contributions tothe public pool, patterns of communication are affected by the racial composition of thegroup, with Black students appearing to be more responsive to communications made byWhite colleagues as opposed to Black colleagues. Hence, communication is not effectiveat sustaining co-operation in racially homogenous Black groups, possibly because com-munication in these groups allows participants to verify the greater diversity on otherdimensions amongst group members

1.1 Identity and Social Exchange

A long line of social psychology literature affirms that group identity or affiliation mattersfor outcomes in society, particularly since individuals tend to favour members of theirown group over outsiders (Tajfel, 1971). The minimal group experiments conducted byTajfel et al (1971) suggested that simple categorization of individuals into groups, evenon the basis of some trivial criterion, was sufficient to induce in-group bias in behaviour.Moreover, Tajfel et al (1971) also found that not only did subjects favour in-groupmembers, but they also actively tried to maximize the difference in the rewards to in-group members relative to outgroup members, even if this required that the total amountof resources for the in-group be sacrificed. This stood in stark contrast to previouswork in this area, which had attributed in-group favouritism to perceived similarities inattitudes and beliefs amongst in-group members relative to outgroup members (Byrne,1969), inter-group conflict over resources (Sherif et al, 1961), or a common identityforged through a common shared fate (Rabbie and Horwitz, 1969).

The minimal group experimental results were attributed to the fact that to the extentthat the self-identity of individuals was at least partly derived from the social identity ofthe groups to which they belonged, individuals would treat in-group members positivelyin order to enhance the positive image of the group, thereby enhancing their own self-identity (Billig, 1973; Messick and Mackie, 1989; Hogg and Abrams, 1988). This isnot conceptually different from the view recently articulated in the economics literatureby Akerlof and Kranton (2002) that an individual gains utility when her actions andthose of others enhance her own self-image. In the event that the social identity of thegroup became unfavourable, individuals would attempt to leave the group (physicallyor psychologically through disassociation) and join a better group (Turner, et al, 1987).

Subsequent work in the minimal group tradition, while cautioning against the gen-

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eralisability of these results to real world groups (Yamagishi et al, 1999), has tried toelucidate the mechanisms which generate the observed in-group bias. One importantmechanism is that group membership or identity must be public knowledge (Hoff andPandey, 2003; Yamagishi et al, 1999). In the limited information setting of social ex-change, that publicly revealed identity should matter makes sense. Individuals rely oncostlessly observable cues such as race and gender to distinguish between individuals,especially in segmented societies where such characteristics hold some social significance.Moreover, because they are costlessly observable, these visual cues are likely to be priv-ileged over other categorizations, such as class or educational background, even whenthe latter might be more relevant. (Chandra, 2003; Cornell and Welch, 1996). Evenrelatively superficial contexts or frames affects behaviour significantly, as long as thestatus of the parties involved in the interaction is publicly revealed. (Ball et al, 2001;Hoff and Pandey, 2003; Eckel and Grossman, 1996;). 1 However, identity per se may beinsufficient to produce an in-group bias unless it comes with an expectation of in-groupreciprocity (Yamagishi and Kiyonari, 2000). 2

More recent experimental evidence from games which mimic social dilemmas con-firms that individuals are more likely to co-operate with an in-group member than anoutsider (Orbell, van der Kragt and Dawes, 1988; Brewer and Kramer, 1986; Kollock,1997; Kramer and Brewer, 1994; Wit and Wilke, 1992). In addition, there is mount-ing experimental evidence that participants in experiments, especially those entailing astrategic interaction, make inferences about the likely behaviour of their partner/s basedon ascriptive characteristics such as race (Glaeser et al, 2000), gender (Scharleman etal, 2001; Chaudhuri et al, 2002; Croson and Buchan, 1999) and ethnicity (Fershtmanand Gneezy, 2001; Brouckaert and Dhaene, 2003). Expectations play an important rolein strategic interactions (Ashraf et al, 2003; Eckel and Wilson, 2003; Hoff and Pandey,2003), and will depend on the social meanings that a context conveys (Kahneman andTversky, 2000; Hoff and Pandey, 2003). This is particularly the case in segmented so-

1For example, in an oral double auction, Ball et al (2001) induce a relatively superficial statustreatment and demonstrate that this has a significant impact on the outcomes. In one treatment,participants were randomly assigned to a high status or low status group, while in a second treatment,assignment depended on the outcome of a trivia quiz (although the scoring mechanism in the quizensured random assignment as well). Individuals who were assigned to the high status group weregiven gold stars to wear, and were treated differentially by the experimenter, receiving applause andpraise. The results show that in a competitive market environment, high status individuals capture alarger share of the surplus, and earn significantly more than low status individuals. Moreover, this resultappears to be driven by a preference on the part of participants to trade with high status individuals.

2In the minimal group experiment tradition, Yamagishi et al (1999) compared a treatment in whichonly the proposer was aware of the group identity of the recipient to the case where both the proposer andrecipient were aware of each other’s group affiliation. If identity on its own was sufficient to producein-group favouritism (as predicted by social identity theory), then the bias should have appeared inboth treatments, but Yamagishi et al (1999) found that an in-group bias only emerged in the lattercase where group membership was mutually known. This was attributed to multilateral fate control orinterdependence in the outcomes of group members (Yamagishi et al, 1999; Karp et al, 1993; Rabbie etal, 1989). In other words, proposers favour in-group members because they expect that this favourablebehaviour will be reciprocated by in-group members, conditional on the identity of the proposer beingknown, thus making the outcomes for group members interdependent. (Rabbie and Lodewijkx, 1994;Pruitt and Kimmel, 1977; Yamagishi et al, 1999; Yamagishi and Kiyonari, 2000).

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cieties, where expectations created by historical and institutional legacies (such as thecaste system in India, apartheid in South Africa) may seriously inhibit the ability ofpreviously disadvantaged groups to enter the economic mainstream (Hoff and Pandey,2003).

While there is some evidence on the impact of heterogeneity on co-operation in apublic goods setting, the majority of these studies have focused on wealth or income het-erogeneity, where such status is experimentally manipulated (Anderson, Mellor and Mi-lyo, 2003; Chan et al, 1999; Cherry, Kroll and Shogren, 2003; Rappoport and Suleiman,1993; and Bergstrom et al, 1986). At best, the evidence on the impact of experimentally-induced income heterogeneity on public goods contributions is mixed. Relatively fewexperimental public goods games have focused on the ways in which real-world hetero-geneity affects co-operation in resolving the public goods problem3. It is in this regardthat this paper aims to make a contribution, but studying the extent to which ascriptivecharacteristics, specifically the racial composition of groups, affects contributions to thepublic good.

2 Experimental Design

A total of 420 participants were recruited from a first year undergraduate Economicscourse at the University of Cape Town 4. Participants played a standard linear publicgoods game which lasted for eight rounds. In each round, participants were allocatedan endowment of ten tokens. 5 The MPCR was set at 0.5 in each round. A secondtreatment was also run, a Communication treatment, in which participants were giventwo minutes at the end of each round to talk to members of their group.

Each group comprised six individuals. Since the focus of this work is on under-standing the ways in which ethnic diversity might affect co-operation in resolving socialdilemmas as represented by the public goods game, the racial composition of the groupswas varied. There were three different race treatments in terms of group composition asfollows:

(1) All White groups

(2) All Black groups

(3) Mixed race groups, comprising three Black students and three White students

The final sample composition is detailed in Table 1. In the event that an individualallocated to a time slot did not arrive, the experimenters randomly recruited replacement

3We are only aware of two studies where the impact of income heterogeneity on co-operation incommon pool dilemmas is studied by using the observed heterogeneity based on the actual attributesof participants. See Cardenas, 2002; and Burns and Visser, 2010.

4Undergraduate Economics classes are typically very large. This sample accounts for a third of thetotal class.

5For final payments, each token had a monetary value of R0.50.

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subjects with the same racial attributes as the missing individual on the spot. 6 Sincestudents were seated in a circle in the same room and entered together, they were ableto directly observe the race and gender of their group members.

Inside each venue, the chairs for participants were arranged in a circle, at somedistance from one another so as to ensure some degree of privacy. As subjects enteredthe room, the experimenter asked them to find a seat. They were then allocated theirplayer identification numbers and told to keep this information confidential. Instructionsheets for the exercises were then handed out, and the experimenter explained the gameto the participants in detail, providing additional examples to illustrate the mechanicsof the game. Once everyone in the group understood the game, the experimenter askedall participants to turn their chairs to face outwards from their group, so that eachparticipant had their back to the group. This was done to ensure anonymity and privacywhen individuals were making their decisions.

Each participant was provided with a Personal Record Sheet which they could use tokeep a record of their decisions in each round of the game as well as their accumulatedearnings as the game progressed, as well as a booklet of individual decision slips, onefor each round. In each round, subjects were asked to record their decision concerninghow many tokens they would allocate to the public account and the private account on adecision slip, and to then fold it in half and hand it to the experimenter once everyone inthe room had finished recording their decisions. The experimenter collected these slipsin random order, and then entered this data directly into a laptop computer, which hada specialized spreadsheet set up to do all the relevant calculations. At the end of eachround, the experimenter would publicly announce to the group the number of tokensthat had been contributed on aggregate to the public account.

Once the game was over, a short post-game questionnaire was handed out, andparticipants were asked to complete the survey while the experimenter got their receiptsready. Subjects were not paid in cash immediately. They were asked to present theirreceipt to the University cashier’s office, located in a building about five minutes walkfrom the location of the experiments. Students were told that the earliest time at whichthey could collect their earnings was the following day. The sessions lasted 45 minuteson average. On average, participants earned R97.49 , although this ranged from R57 toR135. 7 At the time, the hourly tutoring rate paid by the University for undergraduatestudents was R50 per hour.

3 Descriptive Statistics

Table 2 presents descriptive statistics for the sample as a whole, as well as separately forWhite and Black students in the sample. Since racial identity is key to the analysis, wefirst explore whether there are any systematic demographic or attitudinal differencesamongst participants from different race groups, since such differences may provideinsights into the experimental results.

6No session proceeded until a group of six individuals was present. This was done in order to ensurethe MPCR in each game was consistent at 0.5.

7At the time of the experiments, the exchange rate was approximately $US1=R6.50.

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A total of 420 students were recruited to participate in these games. Of these, 53%were Black, while the remainder were White. Males outnumbered females in the sample,although this is driven by gender differences amongst the White sample, and is largely areflection of the demographics of the first year undergraduate economics class . Almostthree quarters of the sample were South African citizens, and this did not vary by racegroup. On average, students knew at most one other student in their experimental group(mean=0.66), and the incidence of friends or tut group members being allocated to thesame group was very low.

3.1 Socio-economic status

There are some clear differences between Black and White students in this sample interms of their socio-economic status. The phenomenon of differences in potential jobexperience by race in South Africa has been documented elsewhere, and is reflectedagain in this sample, with White students being more likely to report having a job thantheir Black counterparts. On average, just under a third of students in the sample hadapplied for financial aid for their studies. However, Black students were far more likely tohave applied for financial aid than their White counterparts. Of those who had appliedfor financial aid, 47% were awarded aid. This is true for 55% of Black students in thesample compared to 9% for White students. 8 In addition to information on financialaid status, students were also given the opportunity to self-classify their own family’sfinancial situation in the post-game questionnaire. Only 4% of the sample classifiedtheir own familys financial status as being rich, and interestingly, no Black studentsin the sample made this selection. The majority of students in the sample classifiedtheir families as either upper income or middle income households, with White studentsclassifying their families predominantly as upper income and Black students classifyingtheir families predominantly as middle income. Of the 4% of students who classifiedtheir families as poor, the overwhelming majority of these were Black.

A third measure of socio-economic status is a behavioural measure linked to theexperiments themselves, namely the time that elapsed between when students playedthe games, and when they presented their receipts to get reimbursed. 9 The averagenumber of days that elapsed between a subject participating in a game and collectingtheir earnings from the cashier’s office was 2.57 days. However, Black students typicallycashed their receipts in within 2 days while White students took just over 3 days onaverage. Moreover, 56% of Black students in the sample collected their cash within aday of having participated in a session, compared with 46% of White students.

These three measures are all suggestive of the poorer socio-economic status of Blackstudents in the sample. Moreover, as Table 3 makes clear, there is some consistency

8There is also some evidence to suggest that there are differences in the kinds of financial aid awardsmade. Only 4% of the sample were awarded financial aid when they had not applied for it in the firstplace. Typically, these kinds of awards reflect merit-based scholarships based on performance in schoolleaving exams. In this regard, the number of awards made to White students is substantially higherthan those made to Blacks.

9The hypothesis is that poorer students or those in greater need of access to cash would be moreeager to cash in their receipts.

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between these three alternative measures. Students who classified their family’s financialstatus as being lower income or poor cashed in their earnings sooner than those whoclassified their families as middle income or better. Similarly, students who had appliedfor financial aid collected their earnings sooner, with 66% of those who had appliedfor financial aid collecting their earnings in the first day. Moreover, over 70% of thosewho classified their families as poor or lower income reported that they had applied forfinancial aid.

3.2 Attitudinal data

The post-game questionnaire also elicited attitudinal responses from participants on arange of questions aimed at measuring trust as well as the propensity to co-operate(Table 2). Moreover, we also collected information pertinent to racial attitudes. Forexample, students were asked for their views on which race groups they thought hadthe best opportunities for getting ahead in South Africa. Over half the sample reportedthat they thought Blacks had the best opportunities for getting ahead, with this viewbeing held by 70% of White students in the sample, compared to only 39% of Blackstudents. Conversely, while just over a fifth of students thought that Whites had the bestopportunities for getting ahead, this view was more commonly held by Black studentsin the sample.

There are some interesting, and sometimes stark differences, in attitudes concerningtrust and co-operation amongst students. On almost every count, the results suggestthat Black students are less trusting of others, have lower risk tolerance and may find co-operative outcomes more difficult to achieve. Over three quarters of the sample agreedthat treating others well would result in favourable treatment in return. However, only72% of Black students held this view compared with 83% of White students in thesample. Trust levels appear to be low, with only a third of the sample agreeing withthe statement that most people can generally be trusted. Only 28% of Black studentsagreed with this statement compared with almost 40% of White students in the sample.Perhaps the starkest difference is revealed in the response to the statement Most people,if they get the chance, will try to take advantage of you. In this instance, 71% of Blackstudents agreed with this statement compared with only 33% of White students . Andfinally, there may be some difference in terms of risk profiles amongst these groups, withalmost double the number of Black students relative to White students reporting theywould never make a high risk investment.

4 Results

To begin, we first consider the average outcomes by treatment, and analyse the resultsusing basic descriptive statistics and graphical plots. This analysis is supplemented byregression analysis focussing both on average group contributions as well as individuallevel contributions. Pooled OLS results are presented, alongside results obtained usinghierarchical linear modeling techniques (HLM). Hierarchical level models are useful whenone is dealing with data that are clustered in some way (Rabe-Hesketh & Skrondal,

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2005). In a repeated public goods game, clustering arises from two distinct sources.The first arises from the fact that participants are nested within groups, and thus, thecorrelation in outcomes for individuals is likely to be stronger within groups than acrossgroups. Secondly, since the game involves repeated decision making over eight rounds,the data are by definition clustered since multiple observations over round are nestedwithin individuals.

These kinds of clustered designs provide a wealth of information on processes thatmight be operating at different levels, for example, individual attributes interacting withgroup dynamics. The key insight, however, is that independence of observations is likelyto be violated owing to the interdependence of observations within the same cluster, beit within groups or individuals. Utilising HLM models helps deal with this issue, andensures robust standard errors. Hence, while we present pooled OLS results for purposesof comparison, in every case, our preferred estimates are those obtained from the HLMmodels.

4.1 Racial homogeneity in a group does not uniformly predicthigher contributions to the public good.

Figure 1 shows the average contributions made to the public pool in each round, con-tingent on the racial composition of the group. While the game theoretic predictionin this strategic setting is that contributions to the public account should be zero, theevidence does not support this. 10 When no communication is allowed, offers are highestin the All White groups followed by the Mixed race groups, with the lowest offers beingmade in All Black groups. In Round 1, participants in All White groups contribute justover 80% of their endowment to the public good, and this declines to just over 52% bythe last round. In mixed race groups, participants begin by contributing 68% of theirendowment to begin with, declining to 44% by the final round. In contrast, participantsin All Black groups begin by contributing 54% in the first round, declining to 30% inthe final round. Hence, on average, subjects in the All White groups contribute 6.49tokens to the public good compared with an average offer of 5.64 tokens for individualsin the mixed race groups, and 4.11 tokens for individuals in All Black groups (Table 5).These differences are statistically significant in all cases. 11

In the communication treatment, the same results hold true although the differencesin contributions to the public pool are not quite as stark. It is apparent that contri-butions to the public pool increase significantly for all groups when communication is

10Typically, participants in public goods games begin by contributing between 40-60% of their en-dowment to the public account, but by the latter rounds in the game, in the face of increased free-ridingbehaviour, contributions to the public account decline almost to zero. (Camerer, 2003;Ledyard, 1995)

11Two sample Wilcoxon ranksum tests as well as t-tests confirm that average offers in All White groupsand Mixed race groups are significantly higher than in All Black groups (White-Black comparison: z=-10.43, p=0.00; t=-10.79, p=0.00; Mixed-Black comparison z=7.58, p=0.00; t= 7.84, p=0.00 ), and thisalso holds true for comparisons by round. Average offers in All White groups are also significantly higherthan Mixed Race groups (z=-4.36, p=0.00; t=-3.95, p=0.00) although closer inspection of comparisonsby round suggests this is largely attributable to differences arising initially in round 1, and later inrounds 5 and 7.

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allowed. This is well demonstrated in Figure 1.12 Moreover, in the first three rounds ofthe game with communication, the differences between the average offers made acrossthe different groups are quite small. In fact, if one compares differences in average offersmade by treatment on a round by round basis, it is clear that in round 3 of the gamewith communication, there is no significant difference in the average offers made bygroup treatment. However, while communication appears to be an effective mechanismfor All White and Mixed Race groups to maintain very high levels of co-operation, itdoes not appear to function as effectively in All Black groups, and by round 4, contri-butions to the public good begin to decline once again for these groups. Consequently,average offers made by individuals in the All White groups are 9.41 tokens and this issignificantly different than average offers made in Mixed race groups at 9.15 tokens (z=-1.96, p=0.05; t=-1.82, p=0.05), although closer inspection of the comparisons by roundreveals that this difference is largely driven by differences arising in Round 1(Table 5).Average contributions in all White groups are also significantly higher than the averageoffer of 8.16 tokens in All Black groups (z=-8.44, p=0.00; t=-7.240, p=0.00), and thisis true both on average and for every round of the game except round 3. Moreover, asbefore, the average offer in Mixed race groups is significantly higher than the averageoffer in All Black groups (z=7.6; p=0.00; t=6.19, p=0.00) on average, and for everyround of the game except round 3.

These results are borne out in the regressions presented in Table 6 13. It is apparentthat in games without communication, offers made by individuals in the All Black groupsare significantly lower than offers made by individuals in All White groups and Mixedrace groups. In other words, there is no evidence of a strong insider effect which shouldmanifest itself as individuals in same race groups being significantly more co-operativethan the mixed race groups. Indeed, the average contributions made by individuals inAll White groups and Mixed race groups are not significantly different from one another.

This finding also holds once communication is allowed. Contributions made by in-dividuals in All Black Groups are significantly lower than offers made in other groups.Notice also that the co-efficients on the treatment dummies are much smaller in thegames where communication is allowed. The co-efficient on All Black group remainssignificant but declines by almost half, while the co-efficient on Mixed Race Group de-clines by almost 70%. This suggests that the attributes or influences that might affectthe contributions made by an individual on average in these different treatment groupsare ameliorated in the presence of communication. Again, there is no uniform stronginsider effect, and the average contributions made by All White groups and Mixed race

12Communication has a large and significant effect on contributions to the public pool. When com-munication is not allowed between rounds, the average offer over all eight rounds in the game is 5.45tokens, compared with an average offer of 8,85 tokens when communication is allowed. This differencerepresents an increase of over 60% in average offers between these two treatments and is statisticallysignificant. (Wilcoxon Ranksum test z=-30.4, p=0.00; ttest=-31.15, p=00). Moreover, a Kruskal Wallistest confirms that the distribution of offers across these two treatments is significantly different. (chi-square=796.77, p=0.00). Finally, the declining trend in the average number of tokens contributed tothe public pool when communication is not allowed, and the strong upward and sustained contributionof tokens when communication is allowed is consistent with previous experimental results.

13Note that here, we present regressions run at the individual level. We have replicated all of ourresults at the group level too.

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groups are not significantly different from one another.

These results raise two obvious questions:

(1.) Why is it that despite homogeneity, All Black groups realise significantly lowerco-operative outcomes?

(2.) Does the behaviour of Black and White students in mixed race groups differ signif-icantly from their colleagues in homogenous groups?

We will address each of these in turn.

5 Why are All Black groups less co-operative?

We hypothesise that racial identity represents more than just race, and that subjects mayrespond to racial identity as a cue regarding other attributes, such as socio-economicstatus, linguistic affiliation, trustworthiness and so on. To the extent that these at-tributes differ by race, and hence by treatment group, this could account for the poorerco-operative outcomes in All Black relative to other groups, and provide some insightas to why communication, to the extent that it may confirm the extent of these otherdifferences, is ineffective in All Black groups.

On the basis of the existing empirical literature, we investigate the impact of threedifferent factors on average group level contributions to the public good. The threehypotheses we investigate are as follows:

(1.) Contributions to the public good are an increasing function of the trust levels withina group. This hypothesis comes directly from the existing applied econometricliterature on economic growth, which suggests that low trust within a country orgroup may undermine co-operative behaviour, and ultimately undermine long termgrowth. Previous experimental work in South Africa suggests that Black SouthAfricans are both less trusting and less trusted by others, including Black partners(Burns, 2010).

(2.) Contributions to the public pool are decreasing in the extent of ethnolinguistic het-erogeneity in the group. Existing empirical evidence suggests that groups char-acterised by greater ethnolinguistic diversity14 may find it more difficult to buildconsensus over society’s priorities, thereby undermining co-operation around public

14Where the text refers to racial diversity, or racial heterogeneity, we mean the physical attributes ofskin colour (phenotype), namely blackness or whiteness. In short, this refers to observable racial iden-tity. Where the text refers to ethnolinguistic diversity, this combines the phenotype of skin colour withlanguage of origin for an individual, thereby introducing additional diversity. Explicitly taking ethno-linguistic diversity into account allows for the possibility that, for example, a Black Portuguese speakingparticipant may hold different preferences and norms to other non-Portuguese Black participants in hisgroup, and thus, may behave differently in this strategic setting.

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good provision. We adopt the standard ELF measure used to measure ethnolin-guistic fractionalisation. 15

(3.) Since public goods are normal goods, provision of public goods is increasing in incomeor wealth status. Thus, in societies characterised by large numbers of low incomeor poor individuals, one might expect to see relative under-provision of public goodscompared to more affluent groups or societies.

Table 6 provides a comparison of the extent of trust, poverty status and ethnolin-guistic diversity by treatment group. It is evident that trust levels are lower in all Blackgroups, but the differences in the means between treatment groups are insignificant.(All White groups vs All Black groups z=-0.79; p=0.43; All Black versus Mixed racegroups z=1.16; p=0.25). 16 Similarly, the extent of ethnolinguistic diversity in All Blackgroups is significantly higher than in All White groups (z=13.55; p=0.00) and Mixed racegroups (z=-7.41;p=0.00), and ethnolinguistic diversity is significantly higher in mixedrace groups compared to All White groups. (z=13.99; p=0.00). And finally, All Blackgroups and Mixed race groups evidence significantly higher levels of socio-economic needon all three measures of socio-economic status relative to All White groups. All Blackgroups evidence significantly higher levels of need relative to Mixed race groups basedon the measures relating to financial aid status. However, in relation to the behaviouralmeasure of need based on time taken to collect earnings, mixed race groups have a higherfraction of individuals who collected their earnings within a day, and this difference issignificant at the 10% level.

In sum, these simple statistics suggest that All Black groups are indeed more het-erogeneous in terms of linguistic orientation, have lower trust levels and higher socio-economic need relative to the other groups. To the extent that each of these factors mayserve to undermine co-operation in the provision of public goods, this would account forthe lower contributions in All Black groups on average. In other words, while race is aclearly observable trait, it may convey information about more than homogenous racialidentity to other group members.

In Tables 7 and 8, we present regression results that include these three institutionalparameters. (Table 7 presents regressions run at the group level, while Table 8 presentsregressions run at the individual level). To begin, we focus on the results from the gameswithout communication. In Column 2, we present results for the same race groups only,

15Posner (2000) has highlighted a few problems with this measure. Posner’s (2000) arguments relate tothe use of this formula as a measure of ethnic fractionalization in cross-country growth studies. Problemsinclude the difficulty of correctly specifying the boundaries along which ethnic fractionalization occur,but more importantly, the problem of ethnic fractionalization being endogenous to social, political andeconomic institutions, which in turn affect growth. Moreover, Posner argues that this index ignoresthe dynamics of inter-group competition and conveys no information about the extent of the divisionsbetween members of different race groups. The most pertinent of these criticisms for this study hasto do with correctly identifying the boundaries along which ethnic fractionalisation occur. For ourpurposes, we rely on the visible racial identity of participants in conjunction with their reported homelanguage. While this latter measure may be a noisy indicator of ethnicity, in the absence of moredetailed information and better computational measures, this will have to suffice.

16Similarly, there are no significant differences in trust levels between mixed race groups and AllWhite groups (z=0.28; p=0.78).

12

in other words, we combine only All White and All Black groups, since a key aspect ofthis work lies in explaining the differential outcomes for same race groups. In Column3, we include Mixed race groups together with the same race groups.

Beginning with Table 7 (group level results), the results presented in Column 2suggest that trust has a significant and positive impact on average contributions to thepublic pool for same race groups, while ethnolinguistic diversity amongst group membersworks in the opposite direction. As the share of individuals in the group who have lowersocio-economic status increases, contributions to the public pool fall, although this resultis insignificant. 17.

Immediately, these results provide insight as to why All Black groups fare so muchworse than All White groups. From the summary statistics presented in Table 6, it isevident that trust levels are higher in All White groups than All Black groups (albeitthe mean differences are insignificant), while the extent of ethnolinguistic diversity andsocio-economic need is significantly higher in All Black groups. Thus, the lack of a stronginsider effect demonstrated by All Black groups may be attributed to the fact that theyare characterised by lower levels of trust and greater ethnolinguistic diversity, with thelatter arguably being the primary driver of the differences here. In short, racial identitymay in fact signal additional information concerning extensive diversity and trust withina particular group, as opposed to signaling homogeneity. In Column 3 of Table 7, wepresent the final set of group level regressions for the games without communication,in which we include both mixed race and groups. The results remain robust to theinclusion of mixed race groups.

The results from the games without communication are replicated in the games withcommunication. However, comparing Columns 2 and 5 from Table 7, it appears thatfor same race groups, trust and ethnolinguistic diversity have an even larger impact onoutcomes in the games with communication. In other words, once communication isallowed, these two features become increasingly important for same race groups. How-ever, if one compares the co-efficient estimates from Columns 3 and 6, notice that theco-efficients in Column 6 are smaller than in Column 3. In other words, for the sampleas a whole, it appears that these features become less important once communicationis allowed. This result can only be attributed to the fact that if these features becomeincreasingly important for same race groups once communication is allowed, they mustbecome significantly less important in explaining the outcomes for mixed race groups.

This raises an interesting puzzle: in same race groups then, it would appear thatcommunication allows individuals to verify the extent of heterogeneity within the group,and this in turn, has a negative impact on contributions to the public pool. In contrast,however, even though communication within mixed race groups should arguably affordindividuals the same opportunity to verify the extent of heterogeneity, the outcome ofthe communication process appears to work in the opposite direction. Why this shouldbe the case is not immediately apparent, and suggests that it may be particularly impor-tant to understand the nature of the communication process, and how this might haveimpacted on outcomes in the games. Statistics describing the nature of the communica-tion process are presented in the next section, once we have verified that the results are

17This holds irrespective of which measure of socio-economic status we use

13

robust at the individual level.Table 8 presents the same regressions, but run at the individual level. In many

respects, the results are very similar to the results presented using group level analysis. 18

Trust has an important positive and significant impact on an individual’s contributionsto the public good, especially for individuals in same race groups. (Note the decline in theco-efficient, albeit small, once mixed race groups are included in the regression analysisin Column 3). While trust remains important even when communication is allowed, themagnitude of the trust co-efficient is substantially smaller (reduced by over 30% whenone considers the entire sample). Contributions to the public good are declining inthe extent of ethnolinguistic diversity in the group, although communication appears tomitigate the negative effects of this heterogeneity for individuals on average19 Moreover,it would also appear that the impact of ethnolinguistic diversity on an individual’scontribution decision is stronger in same race groups than in mixed race groups. Note thedecline in the co-efficient between Columns 2 and 3, and Columns 5 and 6 respectively.Finally, socio-economic status does not appear to have any significant effect on individualcontribution decisions.

Having explored why contributions in All Black groups are lower than hypothesised,we turn to the second outstanding question, namely whether individuals in mixed racegroups behave differently than individuals in homogenous groups. Results presented thusfar already suggest that communication may allow participants in homogenous groupsto verify diversity in other dimensions, thereby undermining contributions, yet, it wouldappear that this same feature is not present in mixed race groups. We focus now onwhy this might be so.

6 Do individuals in mixed race groups behave dif-

ferently than those in homogenous groups?

The obvious question that arises is to what extent behaviour by Whites and Blacks insame race groups differs to behaviour from Whites and Blacks in mixed race groups.In other words, is it just the case that outcomes for mixed race groups reflect theadditive effect of combining White and Black students together, or is there an additionaldifference in contributions by these students once they find themselves in a raciallyheterogeneous group environment.

The results in table 9 present regression estimates that examine this question. Theresults in Columns 2 and 4 suggest that there is no significant difference in the offers

18The key difference between these regressions and those presented earlier in the group level analysisis that here, the variables are measured at the individual level. In other words, the trust variable isa dummy variable that takes a value of 1 if the individual agreed with the statement that Generallyspeaking, most people can be trusted, and zero otherwise. Similarly, the socio-economic status measurerelates directly to the individual as opposed to the share of individuals in the group in need. In contrast,the ethnolinguistic diversity measure used here is identical to that used in the group level analysis, sinceit is difficult to envisage an individual specific measure that would capture this same phenomenon.

19Note that this seems at odds with our group level findings. However, we anticipate that ongoingwork will reveal that ethnolinguistic diversity will have a stronger negative impact on the behaviour ofBlack individuals, consistent with the group level analysis.

14

made by Whites in mixed race groups relative to Whites in All White groups. This istrue both in the games with and without communication. Similarly, in games withoutcommunication, there is no significant difference in the contributions made by Blackstudents, conditional on the racial composition of their group. However, once com-munication is allowed, Black students in mixed race groups make significantly highercontributions to the public pool than Blacks in All Black groups. In other words, thereis something about the communication process within mixed race groups that affectedthe behaviour of Black students in a positive and significant manner, and this featurewas absent or ineffective in the communication process within All Black groups.

As has already been discussed, allowing participants to communicate in public goodsgames has a significant and positive impact on contributions to the public pool. How-ever, in most games with communication, very little (if any) additional data about thequalitative nature of the communication process is collected. In this respect, this studymakes a number of innovations in this regard. During the games with communication,additional data was recorded as follows:

(1.) Total amount of time spent by the group on communication

(2.) Time taken before communication was initiated within a group

(3.) Number of times each individual made a communication intervention in a round

(4.) Length of time each individual spoke for when they made a communication inter-vention in a given round

(5.) Total number of individuals who made a communication intervention in any givenround

This data was recorded using the same specialised computer programme that theexperimenters used to record contributions to the public pool. In the games with com-munication, each screen had an additional graphic that depicted the seating arrangementwithin the room. Each individual’s location within the room was represented by a spe-cific button within this frame. In addition, there was an overall timer button thatwas activated once individuals within the group began communicating with each other.Thus, as soon as communication began, the experimenter activated the overall timerbutton, and also clicked on the individual button for the person who initiated the con-versation. As the conversation progressed, the experimenter was able to click on andclick off the relevant buttons for individuals within the group, thereby recording bothwhich individuals had made a communication intervention and the length of time theyhad spoken for. 20

20Procedures were standardised amongst experimenters to try and minimise errors as much as possibleand to ensure timing measures across groups were as comparable as possible. An experimenter onlyactivated the timer button once an individual within the group had made a communication intervention.In the event that no-one in the group had said anything after five seconds, the experimenter wouldprompt the group to find out if anyone had anything to say. In the event that no-one wanted to sayanything, the experimenter would announce that communication would end five seconds later. In thoseinstances where communication had occurred but the group had reached consensus and gone quiet, the

15

6.1 The number and type of communication interventions isaffected by the race of the individual and the racial com-position of their group

There are some interesting differences that emerge when one examines mean statisticspertaining to communication (see Table 10). On average, individuals made fewer thanone communication intervention per round. However, this varies by race, with Black stu-dents making one communication intervention per round on average compared to lessthan one for White students. These differences are significant (z=7.49, p=0.00; t=8.12,p=0.00). These trends are similarly reflected in the means by treatment group, withthe number of communication interventions being made being relatively higher in theAll Black groups relative to the other two groups. The number of communication inter-ventions made in All Black groups is, on average, significantly higher than in All Whitegroups (z=11.33, p=0.00; t=12.19, p=0.00) and Mixed race groups (z=-11.44,p=0.00;t=-10.64, p=0.00). However, there is no significant difference in the number of commu-nication interventions made between All White and Mixed race groups (z=0.79; p=0.43;t=2.82, p=0.00).

Interestingly though, subjects in the mixed race group are somewhat different thantheir counterparts in the homogenous race group treatments. Black students in mixedrace groups, on average, make half the number of communication interventions as theircounterparts in All Black groups, and this difference is significant (z=-10.85, p=0.00;t=-10.11, p=0.00). Similarly, White students in mixed race groups make almost 30%more communication interventions than their counterparts in All White groups, andthis difference is also significant (z=2.37, p=0.01; t=4.27, p=0.00). Moreover, the num-ber of communication interventions made by White students in Mixed race groups issignificantly higher than those made by Black students on average. (z=-3.13,p=0.00;t=-3.28, p=0.00) In short, Blacks in All Black groups are more likely to make a commu-nication intervention relative to their counterparts in mixed race groups, while Whitesin All White groups are less likely to make a communication intervention than theircounterparts in Mixed race groups.

Similarly, there are differences in terms of the average amount of time spent com-municating by individuals in a given round. On average, total communication time perindividual in a round lasted 6 seconds. However, this differs significantly for Black andWhite students, with the total communication time for white students being only approx-imately 60% of the time spent on communication by Black students (z=4.096,p=0.00;t=5.55, p=0.00). However, note the reversal in this trend for students in the mixedrace groups. Not only do White students in mixed race groups spend almost double theamount of time communicating as their Black counterparts in these groups (this is signif-icant: z=-3.67, p=0.00; t=-4.13, p=0.00), but they also spend longer on communicationthan Whites in All White groups, although this difference is not statistically significant(z=1.05, p=0.29; t=4.89, p=0.00). A similar reversal of behaviour is seen for Black

experimenter would follow a similar procedure. Ten seconds after the last communication interventionhad ended, the experimenter would prompt the group, and in the event that no-one had anything elseto say, would signal the end of communication after a further five second interval had elapsed.

16

students, only in the opposite direction. Black students in Mixed race groups spend lessthan half the time on communication that their counterparts in All Black groups do(z=-9.41, p=0.00; t=-8.14, p=0.00). These differences in communication times by racetranslate into difference in the average communication times by treatment. The averagecommunication time per individual in All Black groups is significantly higher than in AllWhite groups (z=7.23,p=0.00; t=9.07, p=0.00) and Mixed race groups (z=-9.16,p=0.00;t=-7.76,p=0.00), while there is no significant difference in average individual commu-nication times between All White and Mixed Race groups. (z=-1.2, p=0.23; t=3.1,p=0.00).

Since these communication measures relate to the total amount of time an individualspent communicating in any given round (be it in a single or multiple interventions), it ispossible to compute the average communication time per intervention. These figures arepresented in Column 3 of Table 32, and tell much the same story. On average, White stu-dents spend only a quarter of the time per communication intervention that their Blackcounterparts in the sample do. This difference is significant. (z=6.36,p=0.00; t=4.07,p=0.00). This translates into the differences one would expect to see by treatment group.The time per communication intervention is significantly higher in All Black groups com-pared to All White groups (z=11.24, p=0.00; t=9.79, p=0.00) and Mixed race groups(z=-9.91,p=0.00; t=-5.32,p=0.00), while there is no significant difference in the timeper intervention for All White groups compared to Mixed race groups (z=1.49, p=0.14;t=3.51,p=0.00). Once again, however, note the reverse in the trend for students withinMixed race groups. White students in Mixed race groups make significantly longer com-munication interventions on average compared to their counterparts in All White groups(z=3.86,p=0.00; t=5.28, p=0.00) while Blacks in mixed race groups make significantlyshorter interventions on average than Blacks in All Black groups. (z=-10.78, p=0.00;t=-7.77, p=0.00). Moreover, the average length of a communication intervention byWhite students in Mixed race groups is significantly longer than for Black students inthese groups. (z=-4.51,p=0.00; t=-4.12, p=0.00)

Given these findings that the average number of communication interventions aswell as the length of such interventions is higher for Blacks in the sample, it is un-surprising that this translates into the total amount of time spent on communicationbeing significantly higher for All Black groups relative to All White (z=7.82,p=0.00;t=13.61,p=0.00) and Mixed race groups (z=-8.96,p=0.00; t=-11.62,p=0.00). The totalamount of time spent on communication in All Black groups is approximately doublethat of mixed race groups, and almost three times that of All White groups.

These summary statistics provide some preliminary evidence that the communicationprocess in All Black groups was significantly more laboured than in other groups. Moreimportantly though, in mixed race groups, it would appear that White students felt theneed to take charge and dominate the conversation, and that this somehow made a pos-itive difference to the contribution behaviour of Black students within these groups (assuggested by the regression results in Table 9). This suggests to us that communicationwithin All Black groups served to verify the extent of linguistic diversity in particularamongst group members, which ,when combined with lower levels of trust on average,and higher socio-economic need, may have undermined the propensity to co-operate. Incontrast, while the extent of linguistic diversity in mixed race groups was lower than

17

in All Black groups, and trust levels were higher, both of which would have improvedcontributions to the public good, it appears that the communications being made byWhite students within these groups was somehow deemed sufficiently credible or reliableby Black students to induce higher contributions on their part.

7 Discussion

This study has utilised experimental economic techniques to explore the micro-foundationsfor results presented in the economic growth literature 21 that suggest an important rolefor trust and ethnolinguistic diversity in affecting the provision of public goods. Inessence, the results presented here lend credence to this broader literature.

Our results suggest that observable racial identity may convey information aboutextensive heterogeneity as opposed to homogeneity, especially where race is highly cor-related with diversity in other dimensions, such as linguistic diversity. The results alsosuggest that the nature of the communication process is affected both by the race ofthe individual and the racial composition of their group. It is clear that the patterns ofcommunication in same race groups, namely All White and All Black groups, is reversedin mixed race groups. Moreover, it appears that contributions by Black participantsin Mixed race groups are positively impacted by the communication process (which isdominated by White students) whilst no such effect is present for Blacks in All Blackgroups. We speculate that this occurs both because communication allows participantsin All Black groups to verify the greater linguistic diversity amongst other group mem-bers, and because communications made by White students are somehow deemed morereliable or credible by Black students in mixed race groups, than the communicationsmade by Black students in All Black groups.

Taken together, these results clearly point to the importance of openness and trans-parency in policy processes around the provision of public goods, along with the need tofacilitate dialogue, both within and between groups. The latter process becomes partic-ularly important where additional cleavages occur within society, along the dimensionof race for example, where racial identity masks within group heterogeneity. In thisinstance, facilitating between group dialogue (i.e. between white and black) may be oneway to break the negative feedback loop experienced by a particular group.

However, the results also point to the importance of rebuilding internal social capitalin groups that may have previously been systematically discriminated against, especiallywhere such discrimination has eroded intra-group trust. Such efforts must include poli-cies aimed at improving socio-economic status of these groups, since it is apparent thatsocio-economic need and low trust both inhibit co-operative behaviour. In other words,while ethnolinguistic diversity may have a negative impact on public goods provision,

21Despite the large sample size involved, one limitation of this work is that the sample is comprisedentirely of University students from the University of Cape Town. This holds implications for thegeneralisability of the results. This study needs to be replicated with samples comprising non-Universitystudents in order to address this question. However, we are encouraged by the fact that the results inthis study do accord with the large macroeconometric literature in this regard, which might suggestthat the results would be robust to changes in sample composition.

18

this will be exacerbated for groups also characterised by high socio-economic need andlow trust. While policy measures are not able to address the extent of ethnolinguisticdiversity directly (since this would imply selective immigration and marriage policies forexample which are not morally defensible) except through providing forums for open andhonest dialogue, policy is more able to address socio-economic status through variousredistributive and job creation measures. As socio-economic status improves, this willarguably impact intra-group trust and render ethnolinguistic differences less important.This is one of the most important (and difficult) challenges facing African policy makerstoday, as they strive to turn the dream of an African renaissance into reality.

19

Table 1: Experimental Design

29

Table 1: Sample composition by treatment

Treatment No communication Communication allowed

No. individuals No. groups No. individuals No. groups

All White 60 10 54 9

All Black 60 10 78 13

Mixed Race 90 15 78 13

Total 210 35 210 35

20

Table 2: Descriptive statistics

30

Table 2: Sample statistics

All(n=420) Black (n=222) White (n=198)

Variable Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.

White 0.47 0.50 0.00 0.00 1.00 0.00

Female 0.44 0.50 0.50 0.50 0.37 0.48

South African citizen 0.73 0.45 0.73 0.44 0.72 0.45

Lives in residence 0.64 0.48 0.81 0.40 0.45 0.50

Applied for financial aid 0.30 0.46 0.46 0.50 0.11 0.32

Awarded financial aid 0.17 0.38 0.26 0.44 0.07 0.26

Applied for & awarded financial aid 0.47 0.55 0.09

Awarded financial aid but did not apply 0.04 0.01 0.07

Household size 4.83 1.69 5.51 1.80 4.07 1.17

No. of siblings 2.58 1.98 3.45 2.27 1.62 0.91

Has a job 0.28 0.45 0.11 0.32 0.46 0.50

Member of a sports team 0.42 0.49 0.32 0.47 0.54 0.50

Member of a music group/choir 0.09 0.29 0.12 0.32 0.07 0.25

Member of community/volunteer group 0.23 0.42 0.25 0.43 0.21 0.41

Three closest friends are same sex as respondent 0.60 0.49 0.64 0.48 0.56 0.50

Three closest friends are same race as respondent 0.73 0.44 0.72 0.45 0.75 0.43

Three closest friends are UCT students 0.26 0.44 0.25 0.44 0.28 0.45

No. individuals in group known to respondent 0.66 0.89 0.74 0.95 0.57 0.82

No. of friends in group 0.28 0.58 0.27 0.56 0.29 0.60

No. group members in respondent's tutorial group 0.13 0.43 0.11 0.35 0.15 0.51

No. of days elapsed between game and cash collection 2.57 2.96 2.07 2.22 3.15 3.54

Collected cash within 1 day 0.51 0.50 0.56 0.50 0.46 0.50

Did not collect earnings 0.02 0.01 0.03

Self classified family as rich 0.04 0.00 0.09

Self classified family as upper income 0.35 0.17 0.55

Self classified family as middle income 0.46 0.56 0.34

Self classified family as lower income 0.12 0.21 0.03

Self classified family as poor 0.04 0.06 0.01

Whites have the best opportunity to get ahead 0.22 0.35 0.07

Blacks have the best opportunity to get ahead 0.53 0.39 0.70

Attitudinal responses: (based on agreement with statement)

If you treat others well, they will treat you well in return. 0.77 0.42 0.72 0.45 0.83 0.38

Teamwork is always good 0.83 0.38 0.79 0.41 0.87 0.34

Most people can generally be trusted 0.32 0.47 0.28 0.45 0.37 0.48

People are generally quite selfish. 0.68 0.47 0.72 0.45 0.64 0.48

Most people, if they get the chance, will try to take advantage of you. 0.53 0.50 0.71 0.45 0.33 0.47

0.81 0.39 0.78 0.41 0.84 0.37

It is better to co-operate than compete 0.65 0.48 0.71 0.46 0.58 0.49

I would never make a high risk investment 0.21 0.41 0.26 0.44 0.15 0.36

I suspect hidden motives in others 0.49 0.50 0.53 0.50 0.46 0.50

People ought to be motivated by something beyond their own self-

interest

21

Table 3: Measuring socio-economic status

31

Table 3: Alternative measures of socio-economic status

Family financial status No. of days beween participation Collected earings Applied for

(self classified) and cash collection within a day financial aid

Mean % % n

Rich 2.25 0.47 0 17

(1.69) (.51) (.00)

Upper income 2.98 0.49 0.07 144

(3.44) (.50) (.26)

Middle income 2.58 0.49 0.33 190

(2.97) (.50) (.47)

Lower Income 1.67 0.61 0.82 51

(1.36) (.49) (.39)

Poor 1.87 0.8 0.73 15

(2.26) (.41) (.46)

Financial aid status

Did not apply for financial aid 2.95 0.45 295

(3.27) (.50)

Did apply for financial aid 1.7 0.66 125

(1.78) (.48)

22

Figure 1: Contributions to the public pool, by round and treatment

17

Figure 4: Average contributions to public pool, by racial composition of group

Average contribution to public pool

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

10.00

1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8

No communication With Communication

Round

No

. to

ke

ns

co

ntr

ibu

ted

to

pu

bli

c p

oo

l

All White

All Black

Mixed (3:3)

23

Table 4: Average contributions to public good by treatment

33

Table 5: Mean contributions to public pool by treatment

No Communication With Communication

Treatment All Black White All Black White

All White Groups 6.49 6.49 9.41 9.41

Std.dev -(3.79) -(3.79) -(2.00) -(2.00)

n 480.00 480.00 432.00 432.00

All Black Groups 4.11 4.11 8.16 8.16

Std. dev -(2.98) -(2.98) -(3.17) -(3.17)

n 480.00 480.00 624.00 624.00

Mixed Race Groups 5.64 4.69 6.60 9.15 8.89 9.40

Std. dev -(3.51) -(3.40) -(3.37) -(2.43) -(2.71) -(2.08)

n 720.00 360.00 360.00 624.00 312.00 312.00

24

Table 5: Pooled OLS and HLM regressions, controlling for treatment (run at individuallevel)

50

Table 22: Regression controlling for treatment group (individual level)

Significance: *=1%; **=5%; ***=10%; Std error in brackets

Table 23: Regression controlling for

Dep var: No. tokens contributed to public pool No Communication Communication

OLS HLM OLS HLM

Round -0.38 * -0.38 * 0.03 0.03

(0.04) (0.03) (0.03) (0.02)

All Black group -2.37 * -2.37 * -1.25 * -1.25 *

(0.21) (0.61) (0.16) (0.43)

Mixed race group -0.84 * -0.84 -0.26 *** -0.26

(0.21) (0.56) (0.14) (0.43)

Constant 8.21 * 8.21 * 9.27 * 9.27 *

(0.21) (0.45) (0.17) (0.35)

n 1680 1680 1680 1680

Adj R2 0.13 0.04

LogL -4178.33 -3904.49

Wald 204.45 11.72

Chisquare 460.35 213.39

No. groups 35 35

Ho: All Black=Mixed race 71.09 * 7.47 * 38.36 * 6.49 *

25

Table 6: Mean trust, linguistic diversity and socio-economic status by treatment

40

Table 12: Summary statistics on institutional parameters by group

Standard

deviation

in brackets

Descriptive All White All Black Mixed

Fraction who trust 0.33 0.28 0.35

(0.16) (0.18) (0.18)

Ethnolingusitic diversity in group 0.31 0.90 0.83

(0.28) (0.08) (0.07)

Fraction who collect earnings within a day 0.44 0.52 0.56

(0.25) (0.24) (0.27)

Fraction who applied for financial aid 0.12 0.43 0.31

(0.12) (0.20) (0.17)

Fraction self-classified family as poor 0.04 0.24 0.19

(0.07) (0.19) (0.17)

26

Tab

le7:

Con

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good,

contr

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for

trust

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isti

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up

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47

Ta

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19

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*=

1%

; *

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5%

; *

**

=1

0%

; S

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s

Dep

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pool

No

Com

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Com

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Sam

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me

race

All

Sam

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race

All

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HL

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HL

MH

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nd-0

.38

*-0

.38

*-0

.38

*-0

.02

-0.0

20.

03

(0.0

6)(0

.04)

(0.0

3)(0

.06)

(0.0

5)(0

.03)

Frac

tion

in g

roup

who

trus

t3.

02*

3.02

***

4.37

*4.

08*

4.08

*2.

47*

(0.7

3)(1

.77)

(1.4

0)(0

.71)

(1.0

9)(0

.88)

Eth

noli

ngui

stic

div

ersi

ty in

gro

up-1

.67

*-1

.67

***

-2.1

7*

-1.9

9*

-1.9

9*

-1.3

6**

(0.4

7)(0

.93)

(0.8

1)(0

.37)

(0.6

4)(0

.62)

Frac

tion

who

col

lect

ed e

arni

ngs w

ithi

n a

day

-2.1

2*

-2.1

20.

35-0

.93

***

-0.9

3-0

.04

(0.6

9)(1

.67)

(1.0

9)(0

.51)

(0.7

8)(0

.62)

Con

stan

t8.

11*

8.11

*7.

12*

9.31

*9.

31*

8.91

*

(0.4

9)(1

.04)

(0.8

5)(0

.49)

(0.7

2)(0

.65)

n16

0.00

160.

0028

0.00

176.

0017

6.00

280.

00

Adj

. R2

0.48

0.25

logl

-277

.13

-486

.27

-323

.13

-504

.06

wal

d10

7.30

172.

3323

.99

13.6

7

Chi

squa

re55

.25

124.

5817

.37

42.9

7

No.

gro

ups

20.0

035

.00

22.0

035

.00

27

Tab

le8:

Con

trib

uti

ons

topublic

good,

contr

olling

for

trust

,lingu

isti

cdiv

ersi

tyan

dso

cio-

econ

omic

stat

us

(indiv

idual

leve

l)

54

Ta

ble

26

: R

egre

ssio

n c

on

tro

llin

g f

or

all

th

ree

inst

itu

tio

na

l p

ara

met

ers

on

av

era

ge

gro

up

co

ntr

ibu

tio

ns

to p

ub

lic

po

ol

(in

div

idu

al)

Sig

nif

ican

ce:

*=

1%

; *

*=

5%

; *

**

=1

0%

; S

td e

rro

r in

bra

cket

s

Dep

var

: N

o. t

oken

s co

ntri

bute

d to

pub

lic

pool

No

Com

mun

icat

ion

Com

mun

icat

ion

Sam

e ra

ceS

ame

race

All

Sam

e ra

ceS

ame

race

All

OL

SH

LM

HL

MO

LS

HL

MH

LM

Rou

nd-0

.38

*-0

.39

*-0

.38

*0

.03

-0.0

20

.03

(0.0

4)

(0.0

4)

(0.0

3)

(0.0

3)

(0.0

3)

(0.0

2)

Indi

vidu

al i

s tr

usti

ng0

.97

*0

.68

**

*0

.70

**

0.7

5*

0.5

4*

0.5

1*

(0.1

8)

(0.3

9)

(0.3

0)

(0.1

2)

(0.2

2)

(0.1

9)

Eth

noli

ngui

stic

div

ersi

ty i

n gr

oup

-2.1

4*

-2.7

1*

-2.1

9*

-1.3

1*

-1.8

7*

-1.3

1*

*

(0.2

7)

(0.7

8)

(0.7

7)

(0.2

1)

(0.7

5)

(0.6

6)

Indi

vidu

al c

olle

cted

ear

ning

s w

ithi

n a

day

-0.1

40

.05

-0.0

70

.18

0.0

20

.15

(0.1

7)

(0.3

8)

(0.3

0)

(0.1

3)

(0.2

1)

(0.1

9)

Con

stan

t8

.48

*8

.50

*8

.56

*9

.32

*9

.83

*9

.42

*

(0.2

6)

(0.6

1)

(0.6

2)

(0.2

3)

(0.5

8)

(0.5

3)

n1

64

8.0

09

44

.00

16

48

.00

16

80

.00

10

56

.00

16

80

.00

Adj

. R

20

.12

0.0

4

logl

-23

52

.91

-40

94

.88

-24

96

.75

-39

03

.46

wal

d1

21

.58

19

8.8

81

3.1

71

3.1

9

Chi

squa

re2

22

.95

*4

62

.84

*1

17

.00

22

3.1

0

No.

gro

ups

20

.00

35

.00

22

.00

35

.00

28

Table 9: Contributions to public good, conditional on group composition and individual’sown race

51

treatment group and race of individual

Significance: *=1%; **=5%; ***=10%; Std error in brackets

Table 24: Regression

controlling for treatment

group, race of individual and racial composition of peer group

Dep var: No. tokens contributed to public pool No Communication Communication

OLS HLM OLS HLM

Round -0.38 * -0.38 * 0.03 0.03

(0.03) (0.03) (0.03) (0.02)

All Black group -2.37 * -2.37 * -1.25 * -1.25 *

(0.21) (0.61) (0.16) (0.43)

White in mixed race group 0.11 0.11 0.00 0.00

(0.24) (0.59) (0.15) (0.45)

Black in mixed race group -1.8 * -1.8 * -0.51 * -0.51

(0.24) (0.59) (0.18) (0.45)

Constant 8.21 * 8.21 * 9.27 * 9.27 *

(0.21) (0.45) (0.17) (0.35)

n 1680 1680 1680 1680

Adj R2 0.16 0.04

LogL -4167.23 -3902.83

Wald 228.13 15.09

Chisquare 422.82 210.95

No. groups 35 35

All Black=Black in mixed 6.98 * 0.95 13.74 * 3.19 ***

White in mixed=Black in mixed 61.32 * 23.67 * 6.89 * 3.37 ***

29

Tab

le10

:D

escr

ipti

vest

atis

tics

onqual

ity

and

quan

tity

ofco

mm

unic

atio

nby

trea

tmen

t

60

sta

tist

ics

N

o. o

f ti

mes

ind

ivid

ual

tal

ks

per

rou

nd

Len

gth

of

tim

e

ind

ivid

ual

tal

ks

per

rou

nd

Co

mm

un

icat

ion

tim

e p

er

inte

rven

tio

n p

er r

ou

nd

To

tal

com

mu

nic

atio

n

tim

e fo

r g

rou

p p

er r

ou

nd

Tim

e ta

ken

by

gro

up

bef

ore

co

mm

un

icat

ion

beg

an p

er r

ou

nd

All

0

.85

6

.07

3

.59

2

6.7

9

3.1

2

(1

.64

) (1

3.1

3)

(5.5

7)

(36

.83

) (1

1.5

8)

Bla

ck st

ud

ents

1

.06

7

.24

4

.08

3

3.5

5

3.2

2

(1

.83

) (1

4.6

6)

(5.4

8)

(41

.51

) (1

0.9

8)

Wh

ite

stu

den

ts

0.6

0

4.4

6

2.9

7

17

.55

2

.98

(1

.35

) (1

0.4

9)

(5.6

2)

(26

.63

) (1

2.3

6)

All

Wh

ite

0.4

8

3.0

7

2.0

6

14

.60

2

.29

(1

.10

) (6

.69

) (2

.85

) (2

0.2

1)

(2.6

0)

All

Bla

ck

1.3

9

9.3

5

5.0

4

40

.16

2

.94

(2

.03

) (1

6.5

2)

(5.8

5)

(44

.27

) (4

.22

)

Mix

ed r

ace

0.6

4

4.6

2

3.2

0

20

.89

3

.76

(1

.46

) (1

1.4

6)

(6.3

2)

(32

.06

) (1

7.8

0)

Bla

cks

in

mix

ed r

ace

0.5

1

3.2

1

2.1

7

20

.89

3

.76

(1

.28

) (8

.91

) (4

.04

) (3

2.0

7)

(17

.81

)

Wh

ites

in

mix

ed r

ace

0.7

7

6.0

3

4.2

3

20

.89

3

.76

(1

.61

) (1

3.3

9)

(7.8

5)

(32

.07

) (1

7.8

1)

No

tes:

Sta

nd

ard

dev

iati

on

in

bra

cket

s

30

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The Southern Africa Labour and Development Research Unit (SALDRU) conducts research directed at improving the well-being of South Africa’s poor. It was established in 1975. Over the next two decades the unit’s research played a central role in documenting the human costs of apartheid. Key projects from this period included the Farm Labour Conference (1976), the Economics of Health Care Conference (1978), and the Second Carnegie Enquiry into Poverty and Development in South Africa (1983-86). At the urging of the African National Congress, from 1992-1994 SALDRU and the World Bank coordinated the Project for Statistics on Living Standards and Development (PSLSD). This project provide baseline data for the implementation of post-apartheid socio-economic policies through South Africa’s first non-racial national sample survey. In the post-apartheid period, SALDRU has continued to gather data and conduct research directed at informing and assessing anti-poverty policy. In line with its historical contribution, SALDRU’s researchers continue to conduct research detailing changing patterns of well-being in South Africa and assessing the impact of government policy on the poor. Current research work falls into the following research themes: post-apartheid poverty; employment and migration dynamics; family support structures in an era of rapid social change; public works and public infrastructure programmes, financial strategies of the poor; common property resources and the poor. Key survey projects include the Langeberg Integrated Family Survey (1999), the Khayelitsha/Mitchell’s Plain Survey (2000), the ongoing Cape Area Panel Study (2001-) and the Financial Diaries Project.

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