Real Household Income + Calendar Year vs Wave
In my Metrics project, I am trying to explore the causal impact of income on subjective well-being by exploiting real wage fluctuations in the aftermath of the Brexit referendum. I am using data from Waves 8,9 and 10 of the UKHLS. I have merged net household income, month, and year of the household interview with individual-level files (through hidp) at the wave level and then merged these three waves through pidp to create a long format panel. Finally, using the month and year of household interview, I merged my combined panel with the CPI monthly indices.
My question is, that when it comes to analysis should my time variable be the wave or the calendar year? More specifically, if I were to try plotting average real household incomes over time, do I create calendar year-based cross-sections (so 2016, 2017, etc), compute the real wages for those year based cross-sections, and then plot the movement of these averages over the years? I realized that since in each wave, interviews are conducted over 2 years, averaging real wages for Wave 8, for example, would pick up incomes reported in 2016, 2017, and 2018 and by mixing these years, I was worried I would be misrepresenting the actual macro trend.
Closely related then, in my econometric analysis should my time dummies be the wave or the year of the interview?
Finally, if I should create calendar year based cross-sections and then merge to create a panel, what would be the appropriate weight variable to include? Going through the weighting FAQs, I think it should be j_indin_lw but I wanted to double-check if this is still correct if I have to adjust to using calendar year-based samples?
Apologies for these long-winded and perhaps basic queries but I just wanted to check any big and avoidable mistakes!