A longitudinal analysis of the Mahatma Gandhi National Employment Guarantee Act using a Multidimensional Poverty Index
The Mahatma Gandhi National Rural Employment Guarantee Act (MNREGA) has been championed as one of the most successful attempts to provide a universal employment guarantee scheme in the form of a public work programme.
Institutionalized in 2005, it is now the largest public work programme in the world, providing employment for more than 55 million rural households across the Indian state. However, despite its scale and political success, its impact on poverty and destitution has not been well studied.
This paper attempts to study the longitudinal effects of the programme using a Multidimensional Poverty Index (MPI) based on the Alkire-Foster’s methodology. The dataset used was provided by the Young Lives longitudinal study which has been collecting information on child wellbeing in the area of Andhra Pradesh since 2002 and which focuses its attention on two different cohorts of children: The Older cohort and the Younger cohort which will be studied separately.
The author has focussed analysis between the years 2006 and 2014. In order to assess the level of multidimensional deprivation in the population he used the Alkire-Foster’s methodology to construct a Multidimensional Poverty Index which comprises of three equally weighted dimensions divided in ten equally weighted indicators and used the following indicators: Education, Health and Living Standards.
The MPI shows clearly how households participating in the programme tend to have a decrease in Living Standard destitution of 12.8% for the Older cohort and of 8.4% for the Younger cohort.
In order to assess the treatment effect of the MGNREGA, the author performs a Difference-in- Difference for both cohorts. The analysis resulted in a cumulative treatment effect of 8.7% for the Older cohort and 2.4% for the Younger cohort. Finally, some policy recommendations are given in order to better estimate the impact of public work programmess on a multidimensional level in the future.