Hi All,

I am working with the DHS data across five countries and looking at relationship between women's empowerment and children's dietary diversity after adjusting for some important exogenous variables. So, my DV is Food Groups (ranging from 1 to 7) and my IVs include 3 different domains of women's empowerment, wealth index, location, age of child, seasonal droughts, etc. I am trying to also examine if the effect of women's empowerment on number of food groups consumed will differ across different socioeconomic group hence I am investigation an interaction between women'e empowerment and wealth index. Please find the codes I used below and my Stata outputs. I ran both DL-Tobit and Poisson and estimated the margins command afterwards but I am struggling to interpret the margins. I think DL-Tobit might be a better model since the DV variable is count with a lower and upper limit. Please, what am I doing wrong and how can I interpret the interactions after running the -margins- command

Code:
tobit food_group c.att_score#b4 c.att_score#v190 b19 v025 built_population_2014 growing_season_length irrigation drought_episodes [pw=sample_weight] if ID=="MOZ", ll(1) ul(7) cluster (dhsclust)
Code:
Tobit regression                                Number of obs    =      2,303
F(  12,   2291)    =      64.43
Prob > F    =     0.0000
Log pseudolikelihood = -4916.4141               Pseudo R2    =     0.0107

(Std. Err. adjusted for 576    clusters in dhsclust)
    
Robust
food_group       Coef.          Std. Err.      t    P>t    [95% Conf. Interval]
    
b4#c.att_score 
male              .4682713    .2814429     1.66   0.096    -.0836382    1.020181
female          .3964391    .2830491      1.40   0.161    -.1586201    .9514983
                      
v190#c.att_score 
poorest        -.1941122   .3194032      -0.61   0.543    -.8204618    .4322374
poorer          -.2820354   .2949845     -0.96   0.339    -.8604999    .2964291
middle         -.3193704   .3110293      -1.03   0.305    -.9292989     .290558
richer          -.6558261    .301876       -2.17   0.030    -1.247805   -.0638473
richest            0  (omitted)
                      
b19             .0724426   .0110004       6.59   0.000    .0508708     .0940143
v025           -.2106143   .1878946     -1.12   0.262    -.5790755    .1578469
built_pop    .0001429    .0000382      3.74   0.000    .000068       .0002178
grwing_sea .000125   .0000238        5.25   0.000    .0000783     .0001716
irrigation    -.0000334    .000032      -1.04   0.297    -.0000962     .0000294
drought_epi -.0000109   .0000295    -0.37   0.712    -.0000688    .0000471
_cons          2.417986   .3833341      6.31   0.000    1.666268      3.169704
    
/sigma    2.035057   .0595378    1.918304    2.151811
    
330  left-censored observations at food_group <= 1
1,859     uncensored observations
114 right-censored observations at food_group >= 7
Code:
margins, dydx(*) atmeans predict  (e(1,.))
Code:
        
 Delta-method
                 dy/dx          Std. Err.          z    P>z     [95% Conf.    Interval]
        
b4 
female    -.0234546     .0444658    -0.53   0.598     -.110606    .0636968
att_score    .0905979   .0646847     1.40   0.161    -.0361817    .2173775
                      
v190 
poorer    -.0288767     .0680193     -0.42   0.671     -.162192    .1044386
middle     -.041052     .0765348     -0.54   0.592    -.1910574    .1089534
richer    -.1484396      .0759134     -1.96   0.051     -.297227    .0003479
richest     .0647657    .106857        0.61   0.544      -.14467    .2742015
                      
b19         .0467903      .0069769      6.71   0.000     .0331159    .0604647
v025       -.1360349      .1214412    -1.12   0.263    -.3740553    .1019856
built_pop .0000923       .0000248     3.72   0.000     .0000436    .000141
grwng_seas .0000807   .0000155     5.21   0.000     .0000504    .0001111
irrigation   -.0000216     .0000207    -1.04   0.297    -.0000621    .000019
drought_epi  -7.04e-06   .0000191    -0.37   0.713    -.0000445    .0000304
        
Note: dy/dx for factor    levels is the discrete change from the base level.
Code:
poisson food_group c.att_score#b4 c.att_score#v190 b19 v025 built_population_2014 growing_season_length irrigation drought_episodes [pw=sample_weight] if ID=="RWA", irr cluster (dhsclust)
Code:
margins, dydx(*)