The latest official data from 2017 on the Total Fertility Rate (TFR) poses a challenging caveat to the conventional idea that higher education in women leads to lower fertility rates. Certain statistics suggest that illiterate women or those lacking formal education have lower fertility rates than those with education below the primary level. Furthermore, it is postulated that the culture of individual states might wield more influence than education over fertility rates.
The Inverted-J Pattern: Fertility and Education
The correlation between fertility and education appears to follow an inverted-J pattern. The general expectation holds that there is an inverse relationship between education or income and fertility. A higher level of education or income usually corresponds with lower fertility rates. However, a peculiar observation arises where fertility could increase with a marginal rise in education or income. Nonetheless, in the long run, fertility is noted to decline with significant improvements in the levels of education.
Key Findings from the Report
Interesting data comes from Bihar, where the TFR of women who have not completed primary education stands at 4.4, versus 3.7 for illiterate women. Similarly, in Odisha, a state with a relatively low overall fertility rate of just 1.9, the TFR of illiterate women was 2 as compared to a TFR of 3.6-3.5 among those with primary level schooling or below.
At the national level, the TFR for women with an education level below primary was 3.1, compared to 2.9 for illiterate women and 2.4 for those without formal education.
| State | TFR below Primary Level | TFR of Illiterate Women | TFR of Women without Formal Education |
|---|---|---|---|
| Bihar | 4.4 | 3.7 | Not Available |
| Odisha | 3.5-3.6 | 2.0 | Not Available |
| All-India | 3.1 | 2.9 | 2.4 |
The Perspective of Demographers and Population Experts
Demographers and population experts maintain a cautious outlook on these results. They assert that it is premature to draw any conclusions from this data. They argue for the need to observe a similar pattern over three or four years (the above-mentioned pattern is only for the year 2017) before this Inverted J-curve can be validated as a theory.
