India’s sustained economic growth – 6.4% from 1990-2013 – has played a critical role in lowering poverty in the country. The last few decades have seen steady changes in India’s economy as a whole. Between 1990 and 2013, the share of GDP from agriculture roughly halved (from 33% to 18%), while the share of GDP from services increased from 24% to 31%. Yet, the benefits of India’s economic development remain unevenly distributed. Despite the transition from agricultural to non-agricultural employment in rural areas being underway, newly-created small business activities often fail to grow beyond subsistence level.
Economic theory suggests that the provision of financial products to under-served individuals – often termed microfinance – can play a critical role in helping poor households alter their production and employment choices, and ultimately lift them out of poverty. Yet, current empirical evidence on microcredit programs has demonstrated that improving financial access may not be associated with increased in average income and profits.
The goal of this project is to study the economic impacts of improved access to formal finance in rural India through a suite of financial products ranging from microloans to microsavings and microinsurance. We have a collected a very extensive set of data from one of the largest randomized controlled trial evaluating the expansion of a rural financial institutional model (called KGFS) in Tamil Nadu, India. KGFS also provides tailored wealth management advice through local village branches, in order to effectively reach individuals in financially marginalized rural communities. In each district, we identified comparable service areas and then randomized which areas KGFS branches would be opened in first. By comparing treatment and control service areas roughly two years after treatment branches were opened, we can assess the causal impact of KGFS’ presence on the financial inclusion and wellbeing of our surveyed households. Data on borrowing behavior, detailed measures of agriculture and non-agriculture employment, assets, and well-being were collected on a core sample of 4,160 households; however, where possible, we augment this sample with data from up to an additional 14,263 households for which we also have data on savings, income, and poverty.
Requisite Skills and Qualifications:
The RA would use the data already collected and write code to clean the data, generate new variables, and harmonize variables across survey rounds. The RA would also create tables and graphs with the dataset. Finally, the RA would use econometrics software such as R or STATA to run econometric analysis.
- Niam Shah
- Robert Brinkmann