The data used in this analysis were collected between November 2017 and March 2018.Footnote 2 In Côte d’Ivoire, data were collected from November 20th to 25th 2017, and then following this initial collection a replication was conducted in neighboring Ghana from February 18th to the 21st of 2018. In Côte d’Ivoire, we sampled from four different communities including the country’s largest city Abidjan, smaller cities of Bouna and Grand Bassam, and a fishing village outside of Bassam. In Abidjan, we obtained a cluster sample of one relatively high-income neighborhood, the Plateau, which is the primary business district, and one relatively lower income neighborhood (Marcory). In Bassam, we randomly sampled across four different sites. Moreover, we sampled in a relatively rural area and selected a fishing village outside of Bassam (Mondoukou) as well as Bonoua, a smaller town outside of Bassam. In Ghana, we sampled three sites in the greater Accra region including East-Lagon, La Paz, and Jamestown. In terms of income, East-Lagon is urban and relatively wealthier than the other two sites. La Paz is also urban and relatively middle-income while Jamestown is a fishing village and the poorest of all sites.
In choosing our sample sites and respondents, we followed a pre-determined multi-stage sampling process. In selecting sites, we first randomly selected clusters in sample communities, and within each cluster we chose respondents in both the morning and evening, and on the weekend. At each site, surveyors had a randomly assigned list of times ranging from 20 to 120 s that were used to determine the amount of time to move from one site to the next. Within each site, each surveyor moved in a random direction. After moving to a new site, a randomly generated list of 0’s and 1’s was used to determine if they asked a person to participate (= 1) or not participate (= 0). If the first number seen by a surveyor was a 1 then they would ask the first person they saw to participate and if that potential respondent agreed to do the survey then they would interview that person and then move to a new location according to the next randomly determined time and repeat the process. However, if the number was a 0, or the person declined to participate, then the next person was asked to participate if the following number in the list was 1 or skipped if the number was 0, and so forth. Response rates were over 95% in both Côte d’Ivoire and Ghana.
Finally, we conducted a nationally representative survey of American adults. A nationally representative survey provides a point of comparison to our samples from West Africa, and to earlier studies which as noted have used highly selective samples either consisting of university students at two elite universities in Canada and the U.S., or a sample of doctors (Tversky and Kahneman 1981). In contrast, our sample of U.S. respondents is a nationally representative survey of 1000 U.S. adults, conducted by YouGov from March 19th to March 22nd 2018. The experiment was conducted with an original survey using a number of different means of selection (random-digit-dialing, direct mail, web advertising) with each respondent randomly invited to participate in the survey. Respondents were selected to be representative of the U.S. population based on gender, race, age, and education.Footnote 3 The survey asked a number of questions including the two questions regarding the outbreak of a disease and decision to travel 20 min to save $10.
Data collection in Africa was different than in the U.S., as the former used in-person surveys while the U.S. data was sampled using a variety of means. Across all three countries, the questions were the same and in all of the surveys we randomized the order of the possible responses for the disease outbreak so that respondents were not choosing the satisfying answer, which could be a problem if the certain option in the ‘save’ frame is presented first in order. To address this issue, we had two versions of each question with the order of the certain or probabilistic outcomes randomly appearing first and second in the two versions. For the second question, we had two versions, one in which the good in question was set at a high price and the other in which it was set at a low price.
We also altered the dollar amounts in the questions regarding the prices to account for inflation over time, choosing a $10 discount relative to a $250 jacket price and a $30 shirt price in the United States. In the West African samples, we adjusted the values for the discount and the product prices to account for the local currencies and to reflect the lower average income in Côte d’Ivoire and Ghana relative to the United States. However, in all experiments, we kept the same ratio with respect to the differences in the high and low reference price and the hypothetical discount.
Table 3, in the Appendix, provides frequency statistics for our samples from Côte d’Ivoire and Ghana and some population statistics for each country (Ghana Statistical Service 2018, Census Côte d’Ivoire 2018). For the most part, our sample demographics in both countries are close to the population statistics. One exception is with respect to the religious affiliation in Côte d’Ivoire. Fifty-six percent of our Ivorian sample is Christian and 38% is Muslim with the rest either animist or another affiliation. According to the census, 34% of Ivorians are Christian and 43% are Muslim, though the size of the Christian population is disputed. In our case, the oversampling of Christians is because our locations were in the South where there are more Christians, and because our question on religion asked respondents to choose the religion they most closely identify with.
Question 1: Does Framing Matter for Risk Preferences?
Our first question about rationality has to do with risk framing. Table 1, below, presents the questions as they were asked to respondents (English in Ghana and French in Côte d’Ivoire).
As we discussed, prior research such as Tversky and Kahneman (1981) finds discrepancies in preferences due to whether the questions are framed with losses or gains, thus suggesting inconsistencies in preferences for risk biased by the framing of the question. When the question is framed as saving lives, Tversky and Kahneman (1981) find that 72% of their respondents choose the certain outcome rather than the probabilistic choice. However, when the framing changes to the loss/death version, although the outcome remains the same in terms of lives saved and lost, the percent choosing the certain outcome drops to 22%. In the first case, the majority decision is risk averse, but in the second case the majority decision is risk taking, representing an almost complete reversal in preferences based on the frame presented. The change, as they note, represents a profound shift from risk aversion to risk taking and thus contradictory attitudes about risks involving gains versus losses, which are inconsistent with the rational actor model.
The results from our analysis testing the framing effect are presented in Fig. 1. Here, we present the percentage of respondents choosing the certain outcome when the question is framed as ‘lives saved’ and when it is framed as ‘deaths’. In Fig. 1, the left side shows the results when the question is framed in terms of lives saved for all three countries, with the horizontal line placed at 72% to compare with Tversky and Kahneman’s (1981) original finding. Figure 1 shows that 71% of respondents in Côte d’Ivoire and 66% of respondents in Ghana choose the certain response, while in the U.S. 63% choose the certain response, which is nine percentage points lower than what Tversky and Kahneman (1981) found, although still representing a majority of respondents.
In the right panel of Fig. 1, when the question is framed in terms of death, a majority of respondents in Côte d’Ivoire and Ghana still choose the certain (risk averse) response, 57% and 65% respectively, while only 31% choose the certain response in the U.S. under the death frame. In our two African samples, we do not find the almost complete reversal in risk preferences that Tversky and Kahneman (1981) found, but we do find similar reversals in preferences by the treatment in our nationally representative U.S. sample. Critically, then, respondents in the developing country context were much more consistent and less prone to be affected by the framing effect than found by Tversky and Kahneman (1981).
The unadjusted results thus show that in Côte d’Ivoire and Ghana respondents are largely risk averse when the question is framed in terms of saving people and they remain primarily risk averse even when the certain choice is framed in terms of deaths. As a result, and similar to Henrich, Heine and Norenzayan (2010) and Apicella et al. (2014), it seems that the external validity of Tversky and Kahneman (1981) and other such studies is questionable at least for developing countries like Côte d’Ivoire and Ghana.Footnote 4 Building on these results, we next turn to the results from our second experiment.
Question 2: Does A Reference Point Affect Value of Money Versus Time?
In the second question, we consider the framing of a choice compared to high and low reference points. Specifically, in this case, we replicate a question from Tversky and Kahneman (1981) in which people are asked if they would drive 20 min to save $10 dollars varying if the price of the good is high versus low. The questions as presented in Ghana are shown in Table 2.
When the price of the good is low, Tversky and Kahneman (1981) find that 68% respond that they would travel 20 min to save $10, but when the price is high only 29% say they would travel 20 min for $10, or for a difference of almost 40 percentage points. This suggests, as they note, that the value of money is incompatible with the standard analysis of rational consumer behavior.
Once again, however, our results for Côte d’Ivoire and Ghana do not match Tversky and Kahneman’s (1981) original findings. As shown in Fig. 2, we find that 59% of respondents in Côte d’Ivoire said they would travel 20 min to save money if the price of the good was low while 66% said yes for the high-priced item. Similarly, 66% of Ghanaians said they would travel 20 min to save money if the price of the good was low and 73% for the high-priced item. Compared to Tversky and Kahneman’s (1981), as indicated by the horizontal lines in Fig. 2, we find a much greater willingness to pursue the discount regardless of the pricing frame. This is evidence that our sample of African respondents are relatively more consistent with rational choice theory than found in earlier research.Footnote 5 Importantly, the gap between those willing to travel for the discount when the price is high versus low is very small compared to what has been found in earlier studies. Finally, strikingly, the respondents are more (not less) willing to travel for a discount on a high-priced item—the opposite decision relative to U.S. respondents.Footnote 6