I'm currently using triple difference to analyse the effect of minimum marriage age legislation on the prevalence of child marriages and infant mortality, and have a few issues with my controls I'm including and am getting confused.
1) So far I have included controls for age, whether they live in a rural area, whether they have any education, whether they are classed as poor according to their wealth index, and also fixed effects for country, year, ethnicity and possibly year of marriage. Many of my controls are time-invariant, but they do not get omitted when I run my regressions with time FE so is there something wrong with my results - I've seen on other posts that time-invariant variables should end up omitted with FE? Should I just forget about including them anyway if they are time-invariant?
2) I am using reghdfe so am absorbing the fixed effects but should I be including the other controls in the absorb bracket as well - it gave me the same treatment effect estimator whichever way I did but I just wanted to know which way makes my code more accurate.
3) I'm unsure whether I need the marriage year fixed effects - it increases the magnitude and significance of my coefficient but I'm not sure if its actually necessary since I already have year FE - my reason for including it was because a similar paper has but they didn't have a variable for every year like I do (they only had year of survey)
4) I have also included my results table and wanted to check I am interpreting my coefficient correctly - my dependent variable is a dummy for whether an individual is married under 18 - so can I interpret it as: raising the minimum marriage age to 18 causes a 12 percentage point decrease in the prevalence of underage marriage
Thank you
Code:
reghdfe underagemar dchildmar##postreform2 age rural everschool poor, vce(cluster country ethnicityall) absorb(country ethnicityall currentyear marriageyear)
Code:
HDFE Linear regression Number of obs = 4,331,313 Absorbing 4 HDFE groups F( 6, 10) = 195.82 Statistics robust to heteroskedasticity Prob > F = 0.0000 R-squared = 0.5829 Adj R-squared = 0.5828 Number of clusters (country) = 11 Within R-sq. = 0.4114 Number of clusters (ethnicityall) = 136 Root MSE = 0.3229 (Std. err. adjusted for 11 clusters in country ethnicityall) --------------------------------------------------------------------------------------- | Robust underagemar | Coefficient std. err. t P>|t| [95% conf. interval] ----------------------+---------------------------------------------------------------- 1.dchildmar | 0 (omitted) 1.postreform2 | .1263256 .040137 3.15 0.010 .0368949 .2157563 | dchildmar#postreform2 | 1 1 | -.119798 .0419748 -2.85 0.017 -.2133236 -.0262723 | age | -.0790853 .0075066 -10.54 0.000 -.0958111 -.0623595 rural | .0110953 .0047582 2.33 0.042 .0004935 .0216971 everschool | -.019142 .0082793 -2.31 0.043 -.0375895 -.0006945 poor | .011446 .0049752 2.30 0.044 .0003605 .0225314 _cons | 2.070794 .1527688 13.56 0.000 1.730404 2.411184 --------------------------------------------------------------------------------------- Absorbed degrees of freedom: ------------------------------------------------------+ Absorbed FE | Categories - Redundant = Num. Coefs | --------------+---------------------------------------| country | 11 11 0 *| ethnicityall | 136 136 0 *| currentyear | 63 0 63 | marriageyear | 61 1 60 | ------------------------------------------------------+ * = FE nested within cluster; treated as redundant for DoF computation
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