We invited around 20,000 firms that sell their products through an online retailer to answer an online survey. Because of the high turnover rate in this type of platform, we selected firms with recent online activity.
Invitations were distributed using a pop-up window that was shown whenever a seller logged in to his/her profile. To increase the response rate, the online platform offered a monetary incentive (i.e., a discount to be used in online purchases on the website) for users that completed the survey. To link the survey responses to the administrative records, we created unique survey links for each seller.
In the survey, we elicit beliefs on and provide information about, two topics
(1) the expected nominal exchange rate in the next 12 months.
(2) the expected inflation rate in the next 12 months.
The main intervention of our experiment is designed to create exogenous variation in macroeconomic expectations. We randomly divide the sample into three groups: two treatment groups and one control group. The first treatment group receives information about the expected nominal exchange rate 12 months in the future (forex-information group). The second group receives information about the expected inflation rate over the following 12 months (inflation-information group). The information provided to these groups is based on the Survey of Professional Forecasters on Macroeconomic Indicators published bi-monthly by the Reserve Bank of India. Finally, the third group receives no additional information (control group). To measure whether individuals incorporated the information provided to them into their beliefs, we measured their prior beliefs (i.e., before receiving the information) and their posterior beliefs (i.e., after receiving the information).
Note that we are interested in measuring the causal effect of beliefs. We are not interested in measuring the average treatment effect of providing a piece of information -- indeed, this average effect is probably zero, because the same piece of information may make some individuals update upwards and other individuals update downwards.
We will test predictions from simple macroeconomic models. According to these models, when firms expect the local currency to depreciate, they should raise the prices in anticipation. Likewise, when firms expect the inflation rate to increase, they should raise the prices in anticipation. We also expect to test some additional hypotheses informed by these macroeconomic models. For example, we can measure how exposed firms are to the foreign exchange rate (e.g., whether they export their products or import some of their inputs), and then test whether the firms which are more exposed care more about the exchange rate.
Note also that we will have the administrative data from before our intervention. As a result, we can use pre-treatment outcomes for falsification tests (i.e., did the information affect behavior before the information was provided?). We'll be able to use the pre-treatment outcomes as control variables, which will help us to increase power. The pre-treatment outcomes may also be useful for heterogeneity analysis.