For my thesis, I'm working with a panel dataset that tracks individuals within households. To allow for merging, the dataset provides the household ID in the previous round for each individual. What I'm wondering is how to decide based on this information that a household is "the same" in two different rounds of data collection.
As I'm looking at the socio-economic outcomes of various farming practices, my concern is that there are unobservable household characteristics that influence the farmer's returns to farming but then also correlate with various household outcomes, such as education, assets, etc. To control for this, I'd like to use household fixed effects.
However, I'm wondering which features of a household would justify me saying "this is the same household and these effects are fixed over time". For instance, I don't want to use the household head because if the head changes and every other aspect of the household is identical, the impacts on farming outcomes shouldn't be too large. My question hence is whether there are commonly accepted characteristics for identifying households over time?
For reference, the data I'm using is the Living Standards Measurement Survey - Integrated Surveys on Agriculture, by the World Bank.