ABSTRACT
This study examines the impact of urban and rural development on poverty and inequality in India before economic reform. The methodology comprises two dimensions. Modern time series methods are used to uncover the dynamic patterns of urban–rural poverty and income inequality. A machine-learning algorithm is used to determine the causal structure among the development indicators. Our results show that reductions in rural poverty appear to be a more effective in reducing both urban and rural poverty, although the costs of achieving these reductions have not been considered.
Acknowledgements
We acknowledge the Howard G Buffett Foundation and United States Agency for International Development for providing the resources to conduct this study. The views expressed in this article are solely of the authors and any limitations of the research are solely and equally shared by the authors.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
1 The impulses reported here are to a one SE shock in each of the associated series. The shock is positive. In reading the associated and , one should keep in mind that India’s pre-reform policy was to reduce poverty; thus, the shock should be multiplied by −1 to give a reduction in poverty. We leave the shocks in the outputted form and trust the reader to make this mental adjustment.