This paper studies the determinants of personal income including the returns to education.

 

This paper studies the determinants of personal income including the returns to education. In the process this paper estimates how incomes are affected by characteristics such as gender, caste, language etc. Using a maximum likelihood probability model, private returns to education are estimated using data from a Ministry of Finance Survey on Incomes and Savings conducted in 2004-05.  We find that greater levels of education increase both the likelihood of being employed as well as the income earned from work.  However, the returns from elementary (primary and middle) education are quite low. We also find that ceteris paribus women, lower social groups, rural residents, non-English speakers have both significantly lower incomes and significantly lower likelihood of being employed. Our results indicate that education should be geared towards ensuring flexibility in the students’ occupational choices.

Conclusion

Using a recently made available data on incomes we analyze how a range of household, individual and educational factors affects incomes.  We find that the data for India show similar patterns as found for other countries, however the quantum differs.  The key results are as follows:

Non-education characteristics

? Individuals from SC and ST households are likely to have about 10 percent lower incomes than those from non-SC/ST households everything else remaining the same. 

? Women’s incomes are likely to be about a third lower than males having the same household and educational characteristics.  They are also much more likely to be unemployed than males.

? Those who are currently married are likely to have higher incomes and higher likelihood of being employed.  Other results also indicate an interesting association between the marriage and labour markets. 

? Knowledge of the English language has a significant impact on incomes.  Incomes of those who have knowledge of the language are between 18 to 22 percent higher depending upon whether they can merely understand or converse in it.  

? We also find that occupation effects are highly significant and explain a significant part of the income variations.  

Education characteristics

? Compared to illiterates those who have completed primary have 50% greater incomes, those who have completed middle school have incomes greater by 75%, those who have completed schooling have incomes greater by 172%, graduates by 278% and professional courses by 356%

? After correcting for household and individual characteristics and state effects, compared to illiterates those who have completed primary have 31% greater incomes, those who have completed middle school by 45%, those who have completed schooling by 89%, graduates by 136% and professionals by 171%

? After also including fixed occupation effects, compared to illiterates those who have completed primary have incomes greater by 15%, those who have completed middle school by 21%, those who have completed schooling by 42%, graduates by 69% and professional courses by 97%.

? In other words, we find that the returns to greater education increase significantly as the level of education increases.  This would be fine if the returns at the lowest level were high.  However that is not the case and may be an important reason behind the high drop out rate.  We also find evidence that there are significant rigidities in the labour market in the sense that household and occupational factors explain much of the variance in incomes.  With greater education one may be able to break these rigidities, however, that requires children to remain in school.  

For educational policy the message is quite clear: Quality of delivery, and content that enables flexibility in later occupational choice.  This will ensure that rational children can expect to gain from the benefits of formal education, and therefore also remain in school longer. 

Further research issues include an analysis of state-level differences as well as the interaction of education with other factors.  It is clear that quality of education, its appropriateness, the nature of the economy etc. all affect the returns to greater education in a complex manner.  With the public availability of this dataset, and combinging state or district level indicators on education quality from sources such as Selected Education Statistics, we believe that such analysis can throw important insights into the design of education policy and the impact of education on better livelihoods for all.

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