We employed standard principal factor analysis using the seven variables listed in the table after the aggregate index. Six of these variables capture on-farm adaptive capacity, whereas the non-farm enterprise dummy is an indicator of whether the household is totally dependent on farming or can also count on diversification of livelihoods. Clearly, the choice of the variables employed – as well as of the method used to generate the score – is subjective and purely illustrative.<sup>38</sup> In order to ease interpretability, the adaptation index has been rescaled to have a range between 0 and 100. First, note the drastic loss in sample size compared to the household-level data reported in Table 1: this is due to the fact that the agricultural questionnaire is only administered to households that are engaged in agriculture. This reduction in sample size is thus the first element to consider when investigating household _in situ_ adaptation. Second, there is a trade-off between the number of raw variables that enter the estimation model and the final number of observations for which the score will be available. Considering the observations in this sample, 11,564 farming households, only the non-farm enterprise dummy has no missing values. Limiting the index to just seven variables still results in more than 300 missing values in the final adaptation score. Keeping these caveats in mind, once the index has been built, it can be employed either as an alternative outcome variable or as a conditional variable in a mediation analysis or causal machine learning approach in order to investigate issues related to substitution _vs_ . complementarity of adaptation and migration, immobility, and the investigation of transmission channels illustrated in the conceptual framework of Figure 1.

# **4.2 Empirical checklist**

Finally, we provide some simple empirical guidelines for researchers interested in leveraging existing household survey data to shed light on the climate-migration nexus. These can serve as a starting point to evaluate the preliminary feasibility of a research study as well as to orient identification strategy and sort out issues related to variable definition and construction. This empirical checklist is reported in Box 1 and encompasses the key points we have touched upon throughout the paper. Note that the checklist refers to the case in which a researcher intends to use only existing household surveys as the migration data source.

# **Box 1: Empirical checklist for using household surveys to study climate migration**

# **1) Do you have repeated cross-sections or panel data?**

If you have repeated cross-sections, you can consider the adoption of a pseudo-panel approach (Deaton, 1985). If the data are representative at the national and subnational level, you can consider the aggregation of micro information at the spatial level to obtain panel data at that level of aggregation.

> 38 Similarly, not all the raw agricultural variables included in the construction of the score have necessarily to be strictly related to climate change impacts, as their purpose is to proxy the willingness and capacity of households to invest in and implement modern agricultural practices.

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