So far we have seen that output in factory production in Kenya is lower when individuals of different ethnic backgrounds work together, and that the reason appears to be that biased upstream workers under supply downstream workers of other ethnic groups and misallocate intermediate goods across coethnic and non-coethnic downstream workers. We have also seen that distortionary workplace discrimination is greater durings times of conflict, and that firms introduce policies in response in order to reduce workers’ incentive to discriminate. By studying how discriminatory preferences are shaped, and how firms choose their response to distortionary discrimination, researchers can go beyond identifying a source of ethnic diversity effects in production and begin to address why those effects vary across space and time and how profit motives in the private sector can reduce the aggregate effect of ethnic diversity. In the model of taste-based discrimination above, the impact of conflict on output in diverse teams should persist for as long as attitudes towards workers of other ethnic groups are affected. Periods of increased antagonism may entail significant hidden economic costs if “mean reversion” in taste for discrimination is slow . The evolution of output in teams of different ethnicity configurations across the three sample periods was depicted in figure 2. After the introduction of team pay, average output in both homogeneous and mixed teams was steady for the remainder of the sample period, cannabis grow setup suggesting that the impact of conflict on social preferences was long-lived.How did the response to conflict of distortionary discrimination at work vary across individuals?

Modeling θC and θNC as parameter values shared by all workers is a simplification: in reality some workers will have a higher taste for discrimination than others. Figure 9 plots the distribution, across individual suppliers, of the difference in output between homogeneous and mixed teams supplied, before and after conflict began. It appears that most suppliers discriminate against non-coethnic processors during the pre-conflict period. Conflict led to an increase in the output gap between homogeneous and mixed teams supplied for most upstream workers, but also to a notable widening of the distribution of the output gap. The figure indicates that some upstream workers respond more to conflict than others, differentially increasing the extent to which they discriminate against non-coethnics downstream. Some workers in the sample were more exposed to the conflict period of early 2008 than others. Though the workers at the plant and their co-habitating family-members were not themselves directly affected, 22 percent of workers report to have “lost a relative” during the conflict. The decrease in output in mixed teams when conflict began was significantly greater in teams supplied by such workers, as seen in columns 1 and 2 of table 10. These results indicate that personal grievances exacerbate individuals’ workplace response to conflict. Younger individuals may have more malleable social preferences. In columns 3 and 4 of table 10 we see that, although output in homogeneous teams led by old and young suppliers was similar, output in mixed teams with young suppliers was significantly higher during the first year of the sample period. Young suppliers were less discriminatory towards noncoethnic co-workers than old suppliers before conflict began, it appears. This finding is consistent with an expectation expressed by many Kenya commentators before 2008.

It was argued that the young coming of age at the time would be the country’s first “post-tribal” generation . The results of table 10 also show that the decrease in output in mixed teams when conflict began was significantly greater in teams with young suppliers, however. Output in mixed teams with young suppliers was no higher than in mixed teams with older suppliers during the conflict period. These results suggest that youth start out relatively tolerant, but that the attitudes of the young towards non-coethnics respond more negatively to conflict. The results discussed in this section paint a consistent picture of how distortionary attitudes towards workers of other ethnic groups respond to ethnic conflict. It appears that conflict may entail significant hidden economic costs because distortionary social preferences are updated in a “Bayesian” fashion when conflict occurs, at least in the Kenyan context. A serious episode of violent, political conflict between the Kikuyu and Luo blocs led to a significant shift in the average weight attached to the well-being of non-coethnics, a shift that did not decay in the nine months after conflict ended. The negative response was greater among those more affected and among those likely to have a less cemented “prior”.Segregating workers of different ethnic groups would appear to be the profit-maximizing response to distortionary discrimination, from the viewpoint of the econometrician. The results in tables 4 and 8 suggest that segregation would have increased plant productivity by four percent before conflict and by eight percent after conflict began, relative to the status quo of arbitrary assignment to teams. Are these expected benefits of a magnitude that is likely to be salient to supervisors? Consider the output increase expected from optimally assigning workers to teams and positions based on ethnicity, productivity or both. If we view a worker as having three characteristics – the tercile to which she belongs in the distribution of processor productivity, the tercile to which she belongs in the distribution of supplier productivity, and her ethnicity – then an average output will be associated with teams of each of 3 ethnicity configurations, 18 productivity configurations and 63 ethnicity productivity configurations. 

In theory, supervisors can then solve the linear programming problem of maximizing total output subject to the expected output associated with a given type of team and the “budget set” of workers available . The optimal assignments and associated expected output gains are shown in table 11. Throughout the period observed, the output gains expected from assigning workers to teams based on ethnicity were larger than those expected from assigning workers based on productivity – twice as large during the conflict period. In fact segregation achieves about half the output gains of the “complete” solution. The complete solution assigns workers optimally to fully specified teams and thus takes into account interactions between the three workers’ ethnicities and productivities – a complicated “general equilibrium” problem that is likely infeasible for supervisors to solve. It thus appears that the expected productivity gain of segregation is sizable relative to the expected effect of changing other comparable factors under supervisors’ control.It is possible that a similar effect occurs in a Kenyan workplace, although in a situation in which mixed teams are characterized by discriminatory behavior it is also possible that interaction increases tensions and exacerbates ethnic biases. To investigate, I compare the behavior of suppliers with greater versus lower experience working with non-coethnics, in table 12. Focusing on output during the second half of 2007 and the first six weeks of 2008, I contrast teams with suppliers with above-average versus below-average time spent in mixed teams during the first half of 2007. Because most workers at the farm had already spent significant time working with non-coethnics before 2007, columns 3 and 4 restrict the sample to those with below-average tenure. The results show no significant effect of time spent working with non-coethnics on the output gap between mixed and homogeneous teams supplied, vertical grow system neither before nor after conflict began. Workers who have interacted more with individuals of other ethnic groups thus appear no less discriminatory in production. The results in table 12 do not rule out the possibility that complete segregation between the two ethnic groups over time would have a negative influence on attitudes or behavior towards non-coethnics, however. Carrell, Sacerdote, and West find that implementing an estimated optimal assignment can have unintended consequences due to unforeseen responses on the part of individuals to out-of-sample assignments. In the context of the sample farm, in a country that has experienced periodical violent clashes between ethnic groups, and where workers of different ethnic groups reside in the same quarters, complete segregation at the plant could for example lead to increased social tensions on the farm.Nevertheless, it is arguably surprising that a supposedly profit-maximizing firm chose to leave large productivity gains “on the table” by not segregating workers of different ethnicities. Ethical considerations add complexity to the issue of team assignment in Kenya, but we would perhaps expect longer-term costs of segregation to be incurred primarily by society, rather than the firm itself, in which case a case can be made for government intervention to enforce integration within firms. Becker pointed out that discriminatory employers should go out of business as their profits suffer.

A priori, the same argument should hold for flower farms that allow workplace discrimination to influence productivity. However, the floriculture business is not particularly competitive, as evidenced by high profit margins . Moreover, as the literature in macroeconomics on across-firm misallocation has highlighted, it is not necessarily the most productive firms that survive in poor countries’ economies . Further, plant managers did respond to the increase in distortionary discrimination when conflict began, as we have seen. The introduction of team pay for processors was likely motivated by the decrease in productivity in diverse teams in early 2008. It is unsurprising that the dramatic differential decrease in mixed teams’ output when conflict began led managers to respond, even though the lower output observed in diverse teams during the first year of the sample period did not. A doubling of the output gap of diverse teams during a short period of time is likely more salient to managers than potential foregone productivity gains from arbitrary assignment to teams. It appears that managers considered an adjustment to contractual incentives a more desirable response to distortionary discrimination than segregating workers. But note that it is likely not possible to eliminate discrimination through contractual incentives, without entirely breaking the link between workers’ output and pay. At the sample plant, vertical discrimination continued to significantly affect output after the introduction of team pay.Evidence suggests that ethnic diversity negatively affects public goods provision and the quality of macroeconomic policies. While the possibility of an additional, direct effect on micro-level productivity has long been recognized, corresponding evidence is largely absent. In this paper, I begin by identifying a sizable, negative productivity effect of ethnic diversity in teams in Kenya. I do so using two years of daily output data for 924 workers, almost equally drawn from two rival tribes, at a flower-packing plant. The packing process takes place in triangular production units, one upstream “supplier” supplying two downstream “processors” who finalize bunches of flowers. I show that an arbitrary position rotation system led to quasi-random variation in teams’ ethnicity configuration. As predicted by a model in which different weight is attached to coethnic and non-coethnic downstream workers’ utility, suppliers discriminate both “vertically” – undersupplying downstream noncoethnics – and “horizontally” – shifting flowers from non-coethnic to coethnics downstream workers. By doing so, upstream workers lower their own pay and total output. I show that less distortionary, non-taste-based ethnic diversity effects are unlikely to explain this paper’s results. As Becker points out, significant aggregate effects “could easily result from the manner in which individual tastes for discrimination allocate resources within a free-enterprise framework” . Discrimination should lead to misallocation of resources in most joint production situations in which individuals influence the output and income of others. I take advantage of two natural experiments during the time period observed to begin to explore how the productivity effects of ethnic diversity are likely to vary across time and space. When contentious presidential election results led to political conflict and violent clashes between the two ethnic groups represented in the sample in early 2008, a dramatic, differential decrease in the output of mixed teams followed, as predicted by themodel. The reason appears to be that workers’ taste for discrimination against non-coethnic co-workers increased. I estimate a decrease in the weight attached to non-coethnics’ utility of approximately 35 percent in early 2008, through a reduced form approach. A back-of the-envelope calculation suggests that the increase in distortionary workplace discrimination may have cost the plant half a million dollars in annual profit, had it not responded. Six weeks into the conflict period, the plant implemented a new pay system in which downstream workers were paid for their combined output .