The project will be conducted in several districts of Odisha during the agricultural lean season. To implement our experiment, we partner with employers in the village, who typically hire daily-wage laborers for tasks like weeding or house maintenance. We subsidize the cost of the labor for the employers, in exchange for the ability to randomize who is offered work and the level of the wage that is offered. This enables us to hire workers for real jobs with a real employer, while varying wage levels and conditions of the offer. (Note that because we care about the labor supply side only, internal validity is not affected by the fact that the employer is being compensated for his cooperation). In all cases, the employer accompanies our staff when job offers are made, to prove the veracity of the offer. (However, in some treatments the employer is not able to overhear the exact wage offer given to the worker.)
PUBLIC VS. PRIVATE TREATMENTS
As an initial test of hypotheses H1, we randomize wage offers to be at W versus W-10% (i.e. a 10% wage cut below the prevailing wage). To test hypothesis H2, we vary the degree to which others in the village observe the worker’s job acceptance decision. This gives rise to a 3x2 design:
(i) Full public wage cut: wage offer of W-10% on the street (outside the worker’s home) where other workers and employer can observe the answer.
(ii) Partial private wage cut: Wage offer of W-10% in private inside the worker’s home, with the employer present.
(iii) Full private wage cut: Wage offer of W-10% in private inside the worker’s home, with no one else from village present (the employer waits outside, and remains blind to the wage level).
(iv) Full public prevailing wage: public straight offer at W.
(v) Partial private prevailing wage: Wage offer of W in private, with the employer present.
(vi) Full private prevailing wage: Wage offer of W in private, with no one else from village present (the employer waits outside, and remains blind to the offer).
Randomization is at the village level—only one treatment is implemented in a village. We offer the job to five people in each village, and record take-up rates for the job. The laborers who take up work for one day for the employer at the offered wage. The employer provides supervision and in-kind benefits as usual.
H1 and H2 predict that take-up of work under a wage cut will be substantially lower under Treatment (i) than under Treatment (iii). (Treatment (ii) allows us to partial out the extent to which employer observability drives effects).
In contrast, in treatments (iv), (v), and (vi), no community norms against wage cuts are being violated. Consequently, we expect observability to have no impact—providing a useful flasification test. We predict no substantial difference in take-up across these latter 3 treatments. We do expect (iv) and (v) to be weakly higher than (vi) if there are positive peer effects of taking up a job in public, or if there are negative signals associated with turning down a job at W in public or in the presence of an employer.
EVIDENCE ON SANCTIONS AND NORMS
We use two supplementary pieces of evidence to provide positive support that workers face sanctions for accepting work below the prevailing wage.
First, we plan to conduct 10 endline surveys in each village with those approached with a job offer, and a random sampling of control workers who were never approached. We test the spread of information throughout the village and elicit fairness norms (as in Kaur 2015). In addition, we elicit self-reports on what sanctions a worker would face if he took a job.
Second, we will use a set of incentivized lab-based activities with employers and workers, in similar villages to those in our field experiment. We will present lab respondents with the true decisions of anonymous subjects in our field experiments.
In the costly punishment game, we will give the lab respondent an endowment of Rs. 100. He will then be presented with the decision of an anonymized field subject, who will also be given an endowment of Rs. 100. The lab respondent will then be given the opportunity to levy a costly punishment on the field subject. Paying Rs. 1 will result in a decrease in the field subject’s endowment of Rs. 5. The maximal punishment, which costs Rs. 20, leaves the field subject with a final payoff of Rs. 0. Here, we will again vary the treatment and decision of the field subject. Again, we hypothesize that individuals who accept work at Rs. 180 will experience the largest punishments by lab respondents.
Upon completion of the laboratory exercise we will deliver the earnings from these games to the field subjects referenced in the lab activities. The field subjects are not expecting any additional transfer, so receiving any cash will be a positive surprise to them.