We elicited income expectations for three mutually exclusive and exhaustive categories, as displayed in Table 1.

Table 1: Work status categories

To aggregate expectations across categories, we also asked about the likelihood of each scenario. Specifically, we asked the following questions:

1. What is the chance (out of 100%) that you will be in Situation 1 three months from now? That is, you work in a full-time permanent formal job with a regular salary and a written contract and not doing any other job?
2. What is the chance (out of 100%) that you will be in Situation 2 three months from now? That is, you are doing any kind of job or work for payment that is not a permanent formal job. This could include temporary or casual work, piece jobs, self-employment, and family work for pay. 
3. What is the chance (out of 100%) that you will be in Situation 3 three months from now? That is, you will not do any kind of work for pay.

We considered restricting the probabilities to sum to 100 but found this to be impractical over the phone. Instead, we restricted the answer to each question to be between 0 and 100 and then reweighted the probabilities to sum to 100 for each respondent.

For the two employment categories, we then elicited income expectations roughly following the methodology detailed by McKenzie et al. (2013). We asked about the mean, maximum and minimum that jobseekers thought they could earn in each situation in the following way:

1. What salary do you think you would earn in this situation? (Take-home or after deductions). Include all jobs.
2. What is the lowest possible salary you could be earning? (Take-home or after deductions)
3. What is the highest possible salary you could be earning? (Take-home or after deductions)

The only restriction imposed was that the maximum earning had to be larger than the mean and the minimum had to be smaller than the mean earnings.

Finally, we asked the following questions to get an estimate of the shape of the CDF.

4. What do you think the chance is (out of 100%) that you would earn between R ${belief_minimum} and R ${(belief_minimum +belief_maximum)/2} in this situation?

This question provides further information about the shape of the expected income distribution.

Based on this data, we then fitted a uniform and a triangular distribution for each category using questions 2-4. Table 2 shows that the uniform distribution led to slightly higher estimates for both mean and variance of the distribution compared to using a triangular distribution. The direct elicitation fell in between the two detailed elicitations. The correlation between the direct elicitation and the extrapolated values is between 0.86 and 0.92. Taken together, the direct elicitation seems to do a relatively good job at capturing the mean earnings expectation. As we only required the variance estimates for structural estimation, we dropped the more detailed questions after 50% of our survey to limit the burden for our respondents.

Table 2: Comparison of different expectation estimates

Carranza, E., Garlick, R., Orkin, K., & Rankin, N. (2019). Job Search and Matching with Two-Sided Limited Information. Working Paper.

McKenzie, D., Gibson, J., & Stillman, S. (2013). A land of milk and honey with streets paved with gold: Do emigrants have over-optimistic expectations about incomes abroad? Journal of Development Economics, 102, 116–127.