Gender Disparity: Its Manifestations, Causes and Implications

B. Empirical Results for Thailand
The existing literature shows that in many respects Thai women are apparently in a better position compared to their counterparts in South and South East Asian countries including India. Out of 75 provinces, 12 had on an average more family income by the female headed households. The national average of mean income by female household was 86% of that by male headed household in 2001. What is more, the coefficient of variation of the ratio of income by male and female headed household is insignificant. It is true that compared to India, Thailand is certainly a homogenous society in terms of ethnicity, religion and language also. But due to faster dislocation of traditional socio-economic fabric of the society resulting from drastic technological changes and trade orientation, Thai women particularly the unskilled mass are facing depleting employment opportunities in recent period. Thailand has in total 75 provinces, which are comparable to India’s districts. The smaller units make it administratively more accessible. The basic descriptive statistics of limited data base for Thai provinces are presented in Table 12. Note that FMR was less that 1000 only in 27 provinces (36%). But the mean FMR is about 1011 (which is not at all comparable to India), whereas the maximum is about 1088. Moreover, the CV of FMR is negligible, just 3% across the provinces. In all other respects, Thai women fare better than those in India. The correlation coefficients among the selected gender indicators and other economic variables as presented in Table 13 make it clear that role of women in Thai society is unusually high. In many cases, the coefficients are too high to be ignored, but their relationship with sex ratio is reasonably tolerable. This picture is more prominent with the province-wise Spearman’s rank correlation coefficients presented in Table 14. It is natural to find that provinces with higher sex ratio rank very high in terms of female working population, percentages of females formally employed, per capita income. On the other hand, provinces with high year of schooling for male members rank high with both per capita income and per capita consumer expenditure. Figure 9 presents the relationship between female working population and sex ration in Thailand. It is doubtless clear from the diagram that higher proportion of working women is positively associated with higher ex ratio across Thailand provinces. Two observations can be made here. First, male members get higher priority in the family in matters relating to education. Second, there is no high level of disparity between female and male in terms of income and expenditure. It is therefore expected that cross section OLS regression for Thailand provinces for 2001 would generate typical results which uphold gender equality unlike the countries in South Asia. But due to data limitation we could not analyze the rural urban variation.
We have reported here four different models for the determination of sex ratio in Table 15. All the combinations are highly consistent except the negative role played by female life expectancy and the coefficients of the IVs explain a very large proportion (75%) of sex ratio. As obvious from the table, female working population impacts upon sex ratio in a significantly positive way in all the models. Elasticity of income and expenditure is also significantly positive on sex ratio. This finding is theoretically supported by the demographic evolution