Welcome!
I am a development economist and practitioner in the making. I try to understand and change how the world works using data and policy. What do I work on? Social welfare, digital innovation, and poverty eradication.
Currently I am a "Young Professional" at the World Bank working on digital transformation in South Asia. I also conduct analysis on social protection schemes in my free time (yes, I know "nerd alert").
Prior to this role, I was the Interim Deputy Executive Director of J-PAL Africa and Project Director of the Digital Identification and Finance Initiative in Africa (DigiFI) - J-PAL Africa’s first research initiative where I worked with governments, academics, NGOs and private companies to generate and use rigorous research to understand and inform how innovations in digital ID and e-payment policies affect people. I also worked as the Managing Editor for VoxDev at the London School of Economics, as a Country Economist at the International Growth Centre (IGC) Kenya country office, and as a Senior Budget Analyst (ODI Fellow) focusing on social development policies at the South African National Treasury.
I studied international and development economics at Yale University and economics and statistics at St. Xavier's College, the University of Mumbai.
e-mail: nidhi.parekh9@gmail.com
Some of my work
Why is this interesting? The effect of the global recession on poverty substantially changes when we account for social assistance.
Why is this interesting? The poorest are often left behind by social assistance. In this graph, we can see that in sub-Saharan Africa the poorest and richest receive similar proportions of social assistance transfers.
On the other hand, in Latin America and East Asia Pacific the transfers are progressive. Here, the poorest receive a much larger proportion of transfers than the richest.
Why is this interesting?
The literature in economics provides little support to the hypothesis that differences in self confidence can explain differences in labor market outcomes because, against popular stereotypes, if men are from Mars, so are women. This is important because if men and women do not differ on traits such as confidence, it may be that the barriers/opportunities they face are different and that is what needs to be addressed.
The experts’ interpretation of the literature is close to naive pooling and at odds with Bayesian learning. This is especially surprising because for other traits – especially altruism and risk attitudes – the experts’ opinions are more in line with BHM estimates. This raises the question of how experts learn, because, ultimately, this is what determines the advancement of science.