Aishwarya Lakshmi Ratan

Bio: Aishwarya Lakshmi Ratan is an academic researcher from Microsoft. The author has contributed to research in topics: Empirical evidence & Transaction cost. The author has an hindex of 10, co-authored 17 publications receiving 947 citations. Previous affiliations of Aishwarya Lakshmi Ratan include Innovations for Poverty Action & Yale University.

Savings By and For the Poor: A Research Review and Agenda

Abstract: The poor can and do save, but often use formal or informal instruments that have high risk, high cost, and limited functionality. This could lead to undersaving compared to a world without market or behavioral frictions. Undersaving can have important welfare consequences: variable consumption, low resilience to shocks, and foregone profitable investments. We lay out five sets of constraints that may hinder the adoption and effective usage of savings products and services by the poor: transaction costs, lack of trust and regulatory barriers, information and knowledge gaps, social constraints, and behavioral biases. We discuss each in theory, and then summarize related empirical evidence, with a focus on recent field experiments. We then put forward key open areas for research and practice.

Savings by and for the poor: A research review and agenda

Abstract: The poor can and do save, but often use formal or informal instruments that have high risk, high cost, and limited functionality. This could lead to undersaving compared to a world without market or behavioral frictions. Undersaving can have important welfare consequences: variable consumption, low resilience to shocks, and foregone profitable investments. We lay out five sets of constraints that may hinder the adoption and effective usage of savings products and services by the poor: transaction costs, lack of trust and regulatory barriers, information and knowledge gaps, social constraints, and behavioral biases. We discuss each in theory, and then summarize related empirical evidence, with a focus on recent field experiments. We then put forward key open areas for research and practice.

Mobile-Banking Adoption and Usage by Low-Literate, Low-Income Users in the Developing World

Abstract: Due to the increasing penetration of mobile phones even in poor communities, mobile-phone-enabled banking (m-banking) services are being increasingly targeted at the "unbanked" to bring formal financial services to the poor Research in understanding actual usage and adoption by this target population, though, is sparse There appear to be a number of issues which prevent low-income, low-literate populations from meaningfully adopting and using existing m-banking services This paper examines variations across countries in adoption and usage of existing m-banking services by low-literate, low-income individuals and possible factors responsible for the same It is observed that variations are along several parameters: household type, services adopted, pace of uptake, frequency of usage, and ease of use Each of these observations is followed by a set of explanatory factors that mediate adoption and usage

Evaluating and improving the usability of Mechanical Turk for low-income workers in India

Abstract: While platforms such as Amazon Mechanical Turk have generated excitement as a potential source of income in developing regions, to date there remains little evidence that such opportunities have transformed livelihoods for low-income workers. In this study, we analyze the usability barriers that prevent those with basic digital literacy skills from accomplishing simple tasks on Mechanical Turk. Based on our observations, we design new user interfaces that reduce the barriers to task comprehension and execution. Via a study of 49 low-income workers in urban India, we demonstrate that new design elements -- including simplified user interfaces, simplified task instructions, and language localization -- are absolutely necessary to enable low-income workers to participate in and earn money using Mechanical Turk. We synthesize our findings into a set of design recommendations, as well as a realistic analysis of the potential for microtasking sites to deliver supplemental income to lower-income communities.

Managing microfinance with paper, pen and digital slate

Abstract: India's extensive Self-Help Group (SHG) microfinance network brings formal savings and credit services to 86 million poor households. Yet, the inability to maintain high-quality records remains a persistent weakness in SHG functioning. We study this problem and present a financial record management application built on a low-cost digital slate prototype. The solution directly accepts handwritten input on ordinary paper forms and provides immediate electronic feedback. A field trial with 200 SHG members in rural India shows that the use of the digital slate solution results in shorter data recording time, fewer incorrect entries, and more complete records. The paper-pen-slate solution performs as well as, and is strongly preferred over, a purely electronic alternative. The digital slate solution is able to comfortably move between paper and digital worlds, achieving efficiency and quality gains while catering to the preferences and budgets of low-income low-literate clients.

The future of crowd work

Abstract: Paid crowd work offers remarkable opportunities for improving productivity, social mobility, and the global economy by engaging a geographically distributed workforce to complete complex tasks on demand and at scale. But it is also possible that crowd work will fail to achieve its potential, focusing on assembly-line piecework. Can we foresee a future crowd workplace in which we would want our children to participate? This paper frames the major challenges that stand in the way of this goal. Drawing on theory from organizational behavior and distributed computing, as well as direct feedback from workers, we outline a framework that will enable crowd work that is complex, collaborative, and sustainable. The framework lays out research challenges in twelve major areas: workflow, task assignment, hierarchy, real-time response, synchronous collaboration, quality control, crowds guiding AIs, AIs guiding crowds, platforms, job design, reputation, and motivation.

Conducting Clinical Research Using Crowdsourced Convenience Samples

Abstract: Crowdsourcing has had a dramatic impact on the speed and scale at which scientific research can be conducted. Clinical scientists have particularly benefited from readily available research study participants and streamlined recruiting and payment systems afforded by Amazon Mechanical Turk (MTurk), a popular labor market for crowdsourcing workers. MTurk has been used in this capacity for more than five years. The popularity and novelty of the platform have spurred numerous methodological investigations, making it the most studied nonprobability sample available to researchers. This article summarizes what is known about MTurk sample composition and data quality with an emphasis on findings relevant to clinical psychological research. It then addresses methodological issues with using MTurk--many of which are common to other nonprobability samples but unfamiliar to clinical science researchers--and suggests concrete steps to avoid these issues or minimize their impact.

The Future of Crowd Work

Abstract: Paid crowd work offers remarkable opportunities for improving productivity, social mobility, and the global economy by engaging a geographically distributed workforce to complete complex tasks on demand and at scale. But it is also possible that crowd work will fail to achieve its potential, focusing on assembly-line piecework. Can we foresee a future crowd workplace in which we would want our children to participate? This paper frames the major challenges that stand in the way of this goal. Drawing on theory from organizational behavior and distributed computing, as well as direct feedback from workers, we outline a framework that will enable crowd work that is complex, collaborative, and sustainable. The framework lays out research challenges in twelve major areas: workflow, task assignment, hierarchy, real-time response, synchronous collaboration, quality control, crowds guiding AIs, AIs guiding crowds, platforms, job design, reputation, and motivation.