I have a 3 wave panel of children's height and weight and whether either of their parents experienced a change from employment to unemployment (binary) during a recession. As you will see below, I make use of a fixed effects logit model to test this relationship.
BMI Z SCORE
Code:
. xtreg z_score_bmi parents_unemployed_y i.urban_or_rural_y i.year i.mothers_age_y i.mothers_education_y i.
> mothers_marital_status_y child_age_y, fe
note: child_age_y omitted because of collinearity
Fixed-effects (within) regression Number of obs = 28,723
Group variable: id Number of groups = 10,998
R-sq: Obs per group:
within = 0.0476 min = 1
between = 0.0001 avg = 2.6
overall = 0.0140 max = 3
F(12,17713) = 73.74
corr(u_i, Xb) = -0.0131 Prob > F = 0.0000
----------------------------------------------------------------------------------------------------
z_score_bmi | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-----------------------------------+----------------------------------------------------------------
parents_unemployed_y | .0446461 .0200391 2.23 0.026 .0053676 .0839247
1.urban_or_rural_y | .0057002 .0279775 0.20 0.839 -.0491384 .0605388
|
year |
1 | .1900417 .0119574 15.89 0.000 .1666042 .2134793
2 | -.1473351 .0139738 -10.54 0.000 -.1747251 -.1199451
|
mothers_age_y |
19-29 | .0120552 .0357093 0.34 0.736 -.0579385 .0820489
30-39 | .0150572 .0224131 0.67 0.502 -.0288746 .0589889
|
mothers_education_y |
Leaving Certificate to Non Degree | .0791826 .045583 1.74 0.082 -.0101644 .1685297
Primary Degree or greater | .0667024 .0555739 1.20 0.230 -.0422278 .1756327
|
mothers_marital_status_y |
2 | .0312368 .0577671 0.54 0.589 -.0819924 .144466
3 | .0069348 .0835213 0.08 0.934 -.1567751 .1706446
4 | -.0289106 .0350781 -0.82 0.410 -.0976672 .039846
5 | -.117364 .2767071 -0.42 0.671 -.659737 .425009
|
child_age_y | 0 (omitted)
_cons | .6499031 .0508691 12.78 0.000 .5501947 .7496115
-----------------------------------+----------------------------------------------------------------
sigma_u | .90798198
sigma_e | .75347727
rho | .59219609 (fraction of variance due to u_i)
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F test that all u_i=0: F(10997, 17713) = 3.54 Prob > F = 0.0000
Child Overweight
Code:
. xtlogit child_overweight_y parents_unemployed_y i.urban_or_rural_y i.year i.mothers_age_y i.mothers_educ
> ation_y i.mothers_marital_status_y child_age_y, fe nolog
note: child_age_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
----------------------------------------------------------------------------------------------------
child_overweight_y | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-----------------------------------+----------------------------------------------------------------
parents_unemployed_y | .2705773 .1005702 2.69 0.007 .0734634 .4676913
1.urban_or_rural_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
|
mothers_age_y |
19-29 | .1622443 .1849878 0.88 0.380 -.2003252 .5248138
30-39 | .1249393 .1217915 1.03 0.305 -.1137676 .3636462
|
mothers_education_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
|
mothers_marital_status_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
|
child_age_y | 0 (omitted)
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After showing that there is a relationship between parental employment and child weight I want to determine the possible mechanisms of effect.
I suspect that as parents lose their jobs they can less easily afford things for their children like nutritious food, expensive exercise clubs, etc., thus I replicate the above but replace child's weight with things like calorie consumption, sports-club membership etc., as below.
Calorie consumption
Code:
. reg calories3 parents_unemployed_y2 i.urban_or_rural_y2 i.mothers_age_y2 i.mothers_educati
> on_y2 i.mothers_marital_status_y2 child_age_y2
note: child_age_y2 omitted because of collinearity
Source | SS df MS Number of obs = 8,738
-------------+---------------------------------- F(10, 8727) = 56.06
Model | 138278595 10 13827859.5 Prob > F = 0.0000
Residual | 2.1527e+09 8,727 246676.696 R-squared = 0.0604
-------------+---------------------------------- Adj R-squared = 0.0593
Total | 2.2910e+09 8,737 262221.143 Root MSE = 496.67
-------------------------------------------------------------------------------------------
calories3 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
--------------------------+----------------------------------------------------------------
parents_unemployed_y2 | 101.2964 15.69874 6.45 0.000 70.52316 132.0696
|
urban_or_rural_y2 |
Urban | 1.654679 10.93902 0.15 0.880 -19.78839 23.09774
|
mothers_age_y2 |
19-29 | 163.4985 20.73145 7.89 0.000 122.8599 204.137
30-39 | 58.80138 11.90259 4.94 0.000 35.46949 82.13327
|
mothers_education_y2 |
Leaving Certificate to.. | -102.457 22.42631 -4.57 0.000 -146.4179 -58.49615
Primary Degree or grea.. | -254.1752 23.51429 -10.81 0.000 -300.2688 -208.0817
|
mothers_marital_status_y2 |
Married and separated .. | 85.05454 32.51722 2.62 0.009 21.31311 148.796
Divorced / Widowed | 106.224 47.42236 2.24 0.025 13.26496 199.183
Never married | 41.64441 15.10367 2.76 0.006 12.03767 71.25116
Don't know | -94.0545 165.8077 -0.57 0.571 -419.0767 230.9677
|
child_age_y2 | 0 (omitted)
_cons | 1605.807 24.11237 66.60 0.000 1558.541 1653.073
-------------------------------------------------------------------------------------------
Sports Club Membership
Code:
. logit binarysportsclub3 parents_unemployed_y2 i.urban_or_rural_y2 i.mothers_age_y2 i.mothe
> rs_education_y2 i.mothers_marital_status_y2 child_age_y2
note: child_age_y2 omitted because of collinearity
Iteration 0: log likelihood = -6076.5431
Iteration 1: log likelihood = -5886.1443
Iteration 2: log likelihood = -5885.4165
Iteration 3: log likelihood = -5885.4162
Logistic regression Number of obs = 8,773
LR chi2(10) = 382.25
Prob > chi2 = 0.0000
Log likelihood = -5885.4162 Pseudo R2 = 0.0315
-------------------------------------------------------------------------------------------
binarysportsclub3 | Coef. Std. Err. z P>|z| [95% Conf. Interval]
--------------------------+----------------------------------------------------------------
parents_unemployed_y2 | -.4062894 .0653562 -6.22 0.000 -.5343851 -.2781936
|
urban_or_rural_y2 |
Urban | .0365025 .0449827 0.81 0.417 -.0516618 .1246669
|
mothers_age_y2 |
19-29 | -.2919594 .0858323 -3.40 0.001 -.4601877 -.1237312
30-39 | -.0474992 .0488224 -0.97 0.331 -.1431894 .048191
|
mothers_education_y2 |
Leaving Certificate to.. | .7879872 .1000404 7.88 0.000 .5919116 .9840627
Primary Degree or grea.. | 1.189186 .1039254 11.44 0.000 .9854956 1.392876
|
mothers_marital_status_y2 |
Married and separated .. | -.1125892 .1323738 -0.85 0.395 -.3720372 .1468587
Divorced / Widowed | -1.145787 .2191226 -5.23 0.000 -1.575259 -.7163142
Never married | -.1271735 .0619616 -2.05 0.040 -.2486161 -.0057309
Don't know | -.8121472 .695992 -1.17 0.243 -2.176266 .551972
|
child_age_y2 | 0 (omitted)
_cons | -.6890735 .1060969 -6.49 0.000 -.8970197 -.4811274
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My questions are as follows:
- The core analysis is in longitudinal data, but the mechanisms I investigate are only available in 1 of 3 waves in this data, usually later waves when the recession really struck, is it OK to use a cross sectional analysis to investigate the mechanisms I suggest as explaining the results in my longitudinal analysis?
- I wanted to make my cross-sectional analysis almost identical to my longitudinal analysis, but did I succeed? As my longitudinal analysis was a fixed effects regression I wanted to repeat that here and according to this post (https://www.stata.com/statalist/arch.../msg00413.html) including dummy variables is the same thing as including fixed effects, but is that the case, and is there anything I need to do to make this a better replication of my initial fixed effects analysis?
- Also, should I include a variable for year and how could I do this? In my longitudinal analysis I included year fixed effects by including the year variable created by xtset so I feel I should match that in my cross-sectional analysis to keep things the same, but I'm not sure that I have a variable for the year cross-sectionally.
John
0 Response to Using a cross sectional analysis to support the results of a panel analysis
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