I have a 3 wave panel of children's height and weight and parents employment or unemployment (binary) during a recession.

As you will see below, I make use of a fixed effects logit model to test whether a change from employed to unemployed resulted in weight gain in children (a binary obesity outcome). In the model I control for maternal age, education, marital status and urban vs. rural location (all except location are categorical variables). My understanding was that a fixed effects logit regression drops any variables that do not change across waves, so with controls that traditionally don't change, such as adult female education, I was sure that my initial sample of 11,000 children would plummet when I ran the analysis.

As you can see from the below output, this did not happen, instead I have 1,945 groups in my analysis of obesity across the three waves of the study. So my question is, did I misunderstand how xtlogit, fe works? Does it only drop individuals without variation in the outcome? Or do individuals need to have variation in the outcome variable (obesity here), the primary independent variable (parental unemployment here) and the control variables (region, year, mothers age, mother's education, and mother's marital status here) to be included in the regression?

Also does this mean I end up with a really weird sample? i.e. am I limited to saying, "for children whose mothers moved, changed their education and changed their marital status I find that parental unemployment increases weight". This seems like what an analysis that only includes changers in each variable would amount to, but maybe I'm confused?

Thank you for any advice,

John

As you will see below, I make use of a fixed effects logit model to test whether a change from employed to unemployed resulted in weight gain in children (a binary obesity outcome). In the model I control for maternal age, education, marital status and urban vs. rural location (all except location are categorical variables). My understanding was that a fixed effects logit regression drops any variables that do not change across waves, so with controls that traditionally don't change, such as adult female education, I was sure that my initial sample of 11,000 children would plummet when I ran the analysis.

As you can see from the below output, this did not happen, instead I have 1,945 groups in my analysis of obesity across the three waves of the study. So my question is, did I misunderstand how xtlogit, fe works? Does it only drop individuals without variation in the outcome? Or do individuals need to have variation in the outcome variable (obesity here), the primary independent variable (parental unemployment here) and the control variables (region, year, mothers age, mother's education, and mother's marital status here) to be included in the regression?

Also does this mean I end up with a really weird sample? i.e. am I limited to saying, "for children whose mothers moved, changed their education and changed their marital status I find that parental unemployment increases weight". This seems like what an analysis that only includes changers in each variable would amount to, but maybe I'm confused?

Thank you for any advice,

John

Code:

. xtlogit O_obese_y X_eitherparentunemployed_y i.C_region_y i.year i.C_Simplemotherage_y i.C_Simplemothere > duca_y S_age_months_y i.C_mothermar_y, fe nolog note: S_age_months_y omitted because of collinearity note: multiple positive outcomes within groups encountered. note: 9,053 groups (23,188 obs) dropped because of all positive or all negative outcomes. Conditional fixed-effects logistic regression Number of obs = 5,535 Group variable: id Number of groups = 1,945 Obs per group: min = 2 avg = 2.8 max = 3 LR chi2(12) = 239.12 Log likelihood = -1895.5991 Prob > chi2 = 0.0000 ---------------------------------------------------------------------------------------------------- O_obese_y | Coef. Std. Err. z P>|z| [95% Conf. Interval] -----------------------------------+---------------------------------------------------------------- X_eitherparentunemployed_y | .2705773 .1005702 2.69 0.007 .0734634 .4676913 1.C_region_y | -.0209971 .1507919 -0.14 0.889 -.3165439 .2745497 | year | 1 | .3195079 .0596055 5.36 0.000 .2026833 .4363326 2 | -.5625906 .0751008 -7.49 0.000 -.7097854 -.4153958 | C_Simplemotherage_y | 19-29 | .1622443 .1849878 0.88 0.380 -.2003252 .5248138 30-39 | .1249393 .1217915 1.03 0.305 -.1137676 .3636462 | C_Simplemothereduca_y | Leaving Certificate to Non Degree | .4089109 .2192694 1.86 0.062 -.0208492 .8386711 Primary Degree or greater | .4875182 .2805553 1.74 0.082 -.0623601 1.037397 | S_age_months_y | 0 (omitted) | C_mothermar_y | 2 | -.0491349 .2948536 -0.17 0.868 -.6270372 .5287675 3 | -.3627668 .4261465 -0.85 0.395 -1.197999 .4724649 4 | -.1200269 .1744402 -0.69 0.491 -.4619234 .2218696 5 | .762811 1.064853 0.72 0.474 -1.324263 2.849884 ---------------------------------------------------------------------------------------------------- . margins, dydx(X_eitherparentunemployed_y) post Average marginal effects Number of obs = 5,535 Model VCE : OIM Expression : Pr(O_obese_y|fixed effect is 0), predict(pu0) dy/dx w.r.t. : X_eitherparentunemployed_y -------------------------------------------------------------------------------------------- | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] ---------------------------+---------------------------------------------------------------- X_eitherparentunemployed_y | .0622649 .0231713 2.69 0.007 .01685 .1076798 --------------------------------------------------------------------------------------------

## Comment