Hello All,
I am using the NYLS 1979 data set and I have 12,686 individuals over 26 years. I am looking at the impact of working alternate and night shifts on self-reported health outcomes over time and the survey is administered once per year.
I am working under the assumption that the incremental effect of working an additional year in alternate or night shift hours, should increase exponentially over time. For example, the effect of the 1st year to the 2nd year should not be the same as the 8th year to the 9th year. I have included my outcome variable squared and my outcome variable cubed in my model to see how these transformations fit my data. I am unsure if this is the best method to address the issues of the non-linear relationship between health outcomes and alternate shifts over time and I am unsure how to interpret my results. My equation is listed below:
Yit= β0 HealthOutcome+ β1HealthOutcome^2 + β2HealthOutcome^3 + Xit + θt
Yit – health outcomes for individual I in year t
Xit - Vector of demographic controls for individual I in year t
θt – Year fixed effects
stata code is listed below:
xtlogit Health_Limitation_ CumulativeNightShiftYears CumulativeNightShiftYears2 CumulativeNightShiftYears3 i.year Health_Insurance_ Age_at_Interview_ Hours_per_Wk_1 Low_Wage_Worker Some_college Nonwhite Male Ever_Smoke_1998 Aerobic_2002 Married_ weight, vce(cluster id) or
I first ran the code but the results are linear and each additional year of night shift work has the same effect on worker outcomes (contrary to my hypothesis):
xtlogit Health_Limitation_ CumulativeNightShiftYears i.year Health_Insurance_ Age_at_Interview_ Hours_per_Wk_1 Low_Wage_Worker Some_college Nonwhite Male Ever_Smoke_1998 Aerobic_2002 Married_ weight, vce(cluster id) or
Any advice or suggested readings would be greatly appreciated.
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