Primary Outcomes (explanation)

1. Expectations

Changes to the local climate

i. Expected changes in the number of hot days by 2050 (quantitative question).

ii. Expected frequency of large floods by 2050 (quantitative question).

The damage function

iii. Changes in crop yields due to one degree of warming (quantitative question).

iv. Changes in mortality due to one degree of warming (quantitative question).

Expected impacts

v. Expected change in earnings without adaptation (quantitative question).

vi. Expected change in earnings with adaptation (quantitative question).

vii. Expected cost of adaptation (quantitative question).

viii. Expected change in amenity value of locality (quantitative question).

Changes with respect to Dhaka

ix. Expected change in the number of hot days by 2050 relative to Dhaka (quantitative questions).

x. Expected change in mortality due to one degree of warming relative to Dhaka (quantitative questions).

xi. Expected change in earnings without adaptation relative to a city worker (quantitative questions).

2. Intentions relative to oneself

i. Likelihood of changing occupation

ii. Likelihood of migration

iii. Likelihood of investing in adaptation

3. Intentions related to one's children

i. Likelihood child will work in different occupation than the parent

ii. Likelihood child will migrate

iii. Likelihood investment in male child education will increase

iv. Likelihood investment in female child education will increase

4. Policy demand

i. a dummy variable for preferring an adaptation-through-migration intervention to a local adaptation intervention.

ii. a dummy variable for preferring a human capital intervention targeted to children to a local adaptation intervention

iii. willingness to pay for a climate-change information intervention.

We will study the impacts of each intervention on each one of these variables. For interventions (1)-(3) the focus of the analysis will the treatment-control comparison. For interventions (4) and (5) the focus will be the comparison against treatment (3).

To address concerns related to multiple-hypothesis testing, we will calculate indices for each category and sub-category above. We will then report q-values using the p-values on the statistical tests obtained for each summary index. Note that, for the expectation variables, we will construct a different index for each intervention, reflecting the information that was given in that particular intervention. For example, we will exclude expectations about Dhaka from the index calculated for each intervention that does not provide information about Dhaka. While those spill-over effects are of interest and will be reported in the paper, the primary aim here is to test whether each intervention affected the expectations it targeted.

In addition to the indices we will separately estimate the effect of the interventions on expectation 1.a (i.e. expected changes in the number of hot days by 2050). From our experience with the pilot, it has become clear that this question is best understood by the respondents

We will explore treatment effect heterogeneity with respect to the following dimensions:

1. Severity of expected local climate change: an index of village-level dummies for having above median value of all climate variables reported in the information interventions, weighted by sensitivity weights (discussed below).

2. Pre-treatment expectations:

A county-level index obtained using only control observations capturing the average misperception of the likely physical changes and damage function, weighted by sensitivity weights (discussed below).

3. Age: Dummy for whether the age of the respondent is above the median age of the sample

4. Gender: Dummy for being female

5: Income: a dummy for having income above the median

We will focus the analysis of treatment effect heterogeneity on the four main indices listed above. We will also report some exploratory regressions on individual variables if appropriate.

“Sensitivity weights” will let us re-weight different dimensions of climate change according to how threatening individuals perceive them to be. We will calculate these village-level weights by regressing a variable capturing whether individuals think climate change will result in negative damages for the local economy and the lives of local people on climate-change and damage-function expectations. The coefficients from this regression will be used as weights. The larger the coefficient, the larger the weight a given component will be given in the index. For these regressions, we will only use control observations.

Structural Model

We plan to use the exogeneous variation of the experiment to estimate a structural model of migration and occupation decisions. This will enable us to quantify optimal migration and occupation choices and how those are constrained by inadequate information.