Articles by S Chandrasekhar

A chapter dedicated to migration in the Economic Survey 2016–17 signals the willingness on the part of Indian policymakers to address the linkages between migration, labour markets, and economic development. This paper attempts to take forward this discussion. We comment on the salient mobility trends in India gleaned from existing data sets, and then compare and critique estimates of the Economic Survey with traditional data sets. After highlighting the data and resultant knowledge gaps, the article comments on the possibility of using innovative data sources and methods to understand migration and human mobility. It also offers ideas on how an enhanced understanding of mobility is important for policy interventions for those individuals who change locations permanently and those who move seasonally.

The National Sample Survey Office's survey of consumption expenditure is woefully inadequate for estimating the number of food-insecure households in India. Future surveys of NSSO need to collect information on the four pillars of food security: availability, access, nutritional adequacy/utilisation and stability. The Comprehensive Nutrition Survey in Maharashtra is an example of such a survey and appears to do a decent job of capturing the different elements of food security.

Understanding the extent of financial inclusion of rural labour households is important since in the intercensal period 2001-11, the proportion of agricultural labourers in the workforce increased by 3.5 percentage points. This paper examines progress in financial inclusion using information on indebtedness of rural labour households collected by the National Sample Survey Office as part of the surveys of employment and unemployment conducted in 2004-05 and 2009-10. It is estimated that 22.3 million out of the nearly 66 million rural labour households report being in debt in 2009-10. The share of formal institutions in the outstanding debt of rural labour households increased from 29% to 37% while the share of moneylenders decreased from 44% to 33% during this period. What is promising is that the reliance on institutional sources among rural labour households without cultivable land increased from 20.6% to 26%. The aggregate picture however masks large variations across the states and one does not observe any structural change in geographical distribution of flow of credit and share of outstanding advances to the landless.

In an attempt to attain the goal of universal primary education, many developing country governments, including India, have abolished official fees in primary education. The 86th amendment to the Indian Constitution made free and compulsory education a fundamental right for all children in the age group 6-14 years. There are other direct and indirect costs that can deter children from going to school. In this paper, using a rich nationwide data set, the authors construct the incompressible direct costs of attending primary school in India. After controlling for the opportunity cost of going to school (as proxied by the ratio of children's wages to adult's wages), it is found that the direct costs of education adversely affect the probability of children going to school, more so for children from poorer households. The results show that relative to boys, girls are more likely to be affected by the direct costs of schooling. The authors show that making primary education completely free will not increase the attendance rates to 100 per cent. They find that the government will have to incur an additional minimum expenditure of over Rs 2,900 crore every year in order to defray the basic or incompressible cost of attending school.

Inaccurate and biased information on acreage and production of cotton has often in the past led to misleading assessments of value addition of cotton leading to artificial shortages and production losses. New technology such as remote sensing based systems of estimating acreage and production which have been under development for some years, may well provide the necessary tools for more accurate assessments and so to better planning for cotton.