The main effect size of gender is −0.29 (p < 0.05), which indicates it is more difficult for males without nonagricultural jobs to remarry than their female counterparts.
The result shows the main effect size of gender is −0.76 (p < 0.001), which means the remarriage rate of men in rural regions is 53 % (e−0.76−1≈−0.53) lower than that of their female counterparts when other variables are controlled.
The main effect size of gender is −1.25 (p < 0.001), which means the remarriage rate of men with no formal education is 40%% (e−0.51−1≈−0.40) lower than that of their female counterparts when other conditions are held stable.
The result shows the main effect size of gender is −0.46 (p < 0.001), which means the remarriage rate of men is 37%% (e−0.46−1≈−0.37) lower than that of women when they have no minor children.
The result supports hypothesis 4. Interaction of gender and income was added to model 4. The result shows that the main effect size of gender is −0.53 (p < 0.01), which means low income exerts more negative effect on remarriage for males than for females.
In a very recent study, through a meta-analysis carried out on databases from IEA and OECD PISA surveys, Baye and Monseur (2016) compare the effect size of gender gap differences computed at the extreme tails of the achievement distribution with the effect size for the mean scores in reading literacy, mathematics and science.
The estimate in model 5 shows that the main effect size of gender is −1.25 (p < 0.001), indicating that with controls over other variables, the remarriage rate of men with poor housing conditions is 71%% (e−1.25−1≈−0.71) lower than that of their female counterparts.
A survey conducted in the late 1990s in Hebei Province likewise cited gender differences in health, and age was related to the size of gender difference [ 47].
In sites with a small number of female IDUs (Nairobi, Rio, and Santos), there is a great deal more uncertainty in the size of gender differences than in sites with a substantial number of females.
