All you Need is Text. Using Text Mining to Identify Tax Reform Episodes
This session illustrates how text mining can help extract and classify granular information on tax policy actions from tens of thousands of documents, discussing economic developments in 23 advanced and emerging market economies over the last four decades.
Manasa Patnam (SPR)
Mobile Money and Financial Inclusion in India
This talk will explore the impact of mobile money on financial access and economic activity in India. Using granular and high-frequency transaction data from PayTM, one of the largest mobile money firm in India, and combining this with other spatially disaggregated data (such as satellite night-time lights and firm censuses), we document what factors drive the adoption of mobile money, and whether mobile payments help increase the resilience to shocks.
Andrew Tiffin (MCD)
Exploring Causal Relationships with Machine Learning (ML)
The talk will introduce some recent work in the area of “causal” ML using a concrete policy-relevant example—assessing the impact of a hypothetical banking crisis on a country’s growth. Showcasing some specific country examples, we aim to highlight how machine learning can provide an invaluable complement to the skill set of economists within the Fund and beyond.