Primary Outcomes (explanation)

1) Specifically, estimate the following equation by ordinary least squares regression:

1{Bushmeat_ijt} = beta*T_i + alpha*X_i + kappa_j + zeta_t + epsilon_ijt (1)

where Bushmeat_ijt is an indicator that equals 1 if subject i ordered bushmeat and equals 0 if subject i did not order bushmeat, j denotes the table where the subject participated in the experiment, t denotes the date at which the subject participated in the experiment, beta is the coefficient of interest, T_i is an indicator that equals 1 if subject i was in the control group and equals 0 if subject i was in the treatment group, alpha is a vector of coefficients, X_i is a matrix of individual controls, kappa_j are table fixed effects, zeta_t are date fixed effects, and epsilon_ijt is the error term. In all regressions where the unit of observation is an individual subject (a person), we will cluster standard errors at the individual subject level since that is the level at which treatment will be assigned. We include individual-level control variables and fixed effects in order to increase the precision with which beta is estimated. We include the following 8 variables in X_i: age of subject in years, an indicator that equals 1 if the subject is male and equals 0 otherwise, an indicator that equals 1 if the subject has attained university (post-secondary) education and equals 0 otherwise, the number of years of education the subject has, an indicator that equals 1 if the subject has earned a salary in the past 7 days and equals 0 otherwise, an indicator that equals 1 if the subject has earned income as the proprietor of a business in the last 7 days and equals 0 otherwise, the number of times the subject has eaten bushmeat in the last 30 days, and an indicator that equals 1 if the subject eats bushmeat at formal OR informal restaurants and equals 0 otherwise. If an individual-level control variable is missing and the variable is not an indicator variable, we will impute the value with the mean among all non-missing values. For example, if 50 subjects have missing age, we will assume their age equals the mean age among all 550 subjects with non-missing age.

2) Take-up: We will test whether treatment differentially affected coupon use by estimating the following equation by ordinary least squares regression:

1{Use Coupon_ijt} = beta*T_i + alpha*X_i + kappa_j + zeta_t + epsilon_ijt (2)

Where 1{Use Coupon_i} equals 1 if subject i used the coupon (and ordered a dish at their restaurant of choice) and equals 0 otherwise, and the other variables are defined in Equation 1.

3) “Extensive margin” choice of restaurant: We will test whether treatment affects the type of restaurant chosen by estimating the following equation by ordinary least squares regression:

1{Bushmeat Restaurant_ijt} = beta*T_i + eta_1*Dist_ij + eta_2*Price_i + alpha*X_i + kappa_j + zeta_t + epsilon_ijt (3)

Where 1{Bushmeat Restaurant_ijt} equals 1 if subject i chose one of the two restaurants that feature more bushmeat on the sample menu we show to subjects and equals 0 if subject i chose one of the two restaurants that features less bushmeat on the sample menu, eta_1 and eta_2 are coefficients, Dist_ij is the distance in km between subject i’s table j and the restaurant they chose, Price_i is the average price in XOF over all dishes on the sample menu of the restaurant that i chose, and all other variables are defined in Equation 1. We define 1{Bushmeat Restaurant_ijt} and Price_i using the sample menus we show to subjects in the experiment. Specifically, the enumerator will read to the subject, for each restaurant, the different dishes available at that restaurant and the price of each dish in XOF. We will rank restaurants by the percentage of dishes made up by bushmeat. For example, if the sample menu we provide to subjects for Restaurant 1 contains 1 bushmeat dish and 1 non-bushmeat dish, the percentage for Restaurant 1 is 50%. The two restaurants that feature more bushmeat (1{Bushmeat Restaurant_i} = 1) will be the two with the highest percentage. If there is a tie, we will break the tie with the number of bushmeat dishes on the menu. For example, if Restaurant 2 has 2 bushmeat dishes and 2 non-bushmeat dishes on the menu, we would rank Restaurant 2 above Restaurant 1. If there is still a tie, we will calculate the average price of bushmeat dishes at each restaurant, and rank restaurants with less expensive bushmeat dishes above those with more expensive bushmeat dishes. If there is still a tie, we will randomly assign tied restaurants to be defined as featuring more or less bushmeat on the menu.

4) Mechanisms: If subjects who saw a bushmeat video order less bushmeat, why might they have done so?

After watching the video and choosing the restaurant where they want a coupon to, we will ask subjects eight questions related to their view of bushmeat. Each question asks subjects whether they completely disagree, partially disagree, neither disagree nor agree, partially agree, or completely agree with a statement. The first seven statements are of the form: bushmeat is…. And the seven individual attributes are: sustainable (durable), fresh (fraîche), tasty (savoureuse), cool (cool), legal (légale), healthy (saine), and connects them to their place of origin (relie à votre lieu d’origine). The eighth statement is that they are proud of the environment of Democratic Republic of Congo (fier de l'environnement de la République démocratique du Congo).

We aggregate responses to these eight questions into a single index. For each question, we code a value of 1 if the response indicates a negative view of bushmeat. For each of the first seven statements, if the subject partially OR completely disagrees with the given statement we code the response as 1, and if the subject neither disagrees nor agrees, partially agrees, or completely agrees we code the response as 0. For the eighth and final statement, if the subject partially agrees or completely agrees we code the response as 1, and code the response as 0 otherwise. Then we sum the responses over the eight questions for each subject. Thus each subject can have a value of between 0 and 8. Finally we standardize the summed score by subtracting the mean summed score across all subjects, and then dividing by the standard deviation of the summed score across all subjects.

We estimate the following equation by ordinary least squares regression:

StdScore_ijt = beta*T_i + alpha*X_i + kappa_j + zeta_t + epsilon_ijt (4)

where StdScore_ijt is subject i’s standardized response score and all other variables are defined in Equation 1.

To further understand any potential mechanisms, we will also estimate the effect of treatment assignment on each of the eight variables individually. The dependent variable in each regression will be 1{Unfavorable View_i}, an indicator variable that equals 1 if subject i partially OR completes disagrees with the given statement (in the case of the first seven statements). In the case of the eighth statement regarding pride in Congo’s environment the indicator variable will equal 1 if the subject partially agrees or completely agrees. We will therefore estimate eight equations of the following form by ordinary least squares regression:

1{Unfavorable View_i} = beta*T_i + alpha*X_i + kappa_j + zeta_t + epsilon_ijt, (5)

5) Social desirability bias: Several times during the primary experiment the enumerator will instruct the subject that they should order whatever dish they most want to eat at the restaurant, whether that dish be bushmeat or not. Nonetheless, it is still possible to feel concerned that treated subjects may order less bushmeat due to experimenter demand effects, rather than due to having been persuaded by the bushmeat video. We therefore implement the method of Dhar et al. (2022; American Economic Review) in order to assess the threat that experimenter demand effects pose to our interpretation of beta in Equations 1 and 2. Dhar et al. (2022) “use a 13-question short form of the Crowne and Marlowe (1960) module developed by Reynolds (1982).” Given each statement, subjects answer whether they completely agree, partially agree, neither agree nor disagree, partially disagree, or completely disagree. We code the response to each statement as 1 if the subject gives a socially desirable answer. For example, if a subject completely disagrees OR partially disagrees with the statement “I sometimes feel resentful when I don’t get my way”, we would code their response as 1 (and code the response as 0 if they neither disagree nor agree, partially agree, or completely agree). We will sum the coded responses over statements, so that subjects have a social desirability score of between 0 and 13. Then we will standardize the score by subtracting the mean score across all subjects, and then dividing by the standard deviation of the score across all subjects. We augment Equation 1 by adding two variables: the standardized social desirability score, and the interaction of the treatment indicator and the standardized social desirability score. If the coefficient on the interaction term is not different from 0, then it is less likely that social desirability bias/experimenter demand effects are differentially changing what treated subjects order at restaurants. We estimate the following equations by ordinary least squares regression:

1{Bushmeat_ijt} = beta*T_i + sigma_1*StdSoc_i + sigma_2*T_i*StdSoc_i + kappa_j + zeta_t + epsilon_ijt, (5)

where sigma_2 represents the coefficient of interest for assessing differential experimenter demand effects, StdSoc_i is the standardized social desirability score of subject i, and all other variables are defined in Equation 1.