If you have panel data, continue to the next questions.

# **2) What type of migration information do you have access to?**

If you have information on the motives, timing, and destination of migrant individuals, then you can reasonably distinguish between the different types of migration – short- _vs_ . long-term migrants; internal _vs_ . international migrants; voluntary (economically-motivated) _vs_ . involuntary (survival/low-return) migration, etc. In this case, you can use these different migration phenomena as dependent variables and compare the results across different categories and against existing evidence and theoretical predictions. In any case, be aware that when using nationally-representative surveys it is unlikely that you will observe a sufficiently large sample size of international migrants.

If you only have partial access to this information, stick with general definitions of migration. For example, if information regarding timing and reasons of migration is unavailable or unreliable, but you have information on destination, build migration outcomes by imposing a ‘spatial threshold’ after which moving individuals are considered as migrants rather than as simply absent (e.g., an individual could be considered a migrant if he/she has at least moved to another district or subnational administrative unit, even if you do not have data on where or when they moved).

# **3) Do you have a ‘rare event’ issue at the individual level?**

If yes (e.g., migrant individuals are less than 10 percent of the sample), collapse the individual information at the household level and use households as the main unit of analysis. If you still prefer to work at the individual level, adopt data-driven techniques to tackle the data imbalance issue (e.g., Chawla et al., 2002) and divide your original sample into disjointed training and testing sets to assess treatment effects on the original real-world (skewed) data rather than on the artificially rebalanced dataset.

If not, carry out the analysis at both the individual and household level, and compare the key findings. Remember, however, that aside from technical issues, household-level analysis should be preferred for both risk-diversification migration in response to slow-onset changes and ‘survival’ migration in response to fast-onset shocks.

# **4) Does the survey include an agricultural questionnaire?**

If yes, use agriculture-related variables as the most important transmission channels and to explore treatment effect heterogeneity. In addition to the migration outcome, also build an _in situ_ adaptation outcome (e.g., via factor analysis starting from raw agricultural data on inputs, practices, and assets) which you can use as an alternative outcome or as a conditioning variable in order to study the dynamic relationship between migration and local adaptation or the relevance of climate-induced liquidity constraints and immobility traps.

If not, stick with the main migration outcome but be aware that, due to the predominant importance of the agricultural linkage, you may not be able to thoroughly explore treatment effect heterogeneity and mediating channels. In practice, looking only at _average_ treatment effects may be misleading if there is substantial heterogeneity whose underlying signal you are unable to detect (e.g., simplistic case: half sample trapped in immobility, half sample responding positively by sending migrants, null average effect).

# **5) Are you more interested in slow-onset or fast-onset events?**

If you want to explore responses to slow-onset changes, focus on treatment effect <u>heterogeneity, as the literature indicates that average treatment effects can mask substantial</u> 28