Antibiotic effectiveness, forests, and climate stability are three of the most endangered public and common goods of the twenty-first century. All three are threatened by individuals ignoring the negative consequences of their actions on society: spreading antibiotic resistance, increasing forest degradation, and accelerating climate change. All these effects are likely to have long-lasting impacts on global health and economic development. This dissertation seeks to understand these human–environment interactions better while evaluating policies promoting sustainable behaviors or improving economic resilience. The first chapter considers the trade-off in prescribing antibiotics: they cure bacterial infections, but they spur antibiotic resistance.
I estimate two essential parameters to calibrate any model of antibiotic resistance: the causal impact of prescriptions on antibiotic resistance and the elasticity of demand for an antibiotic. After developing and calibrating a dynamic bio-economic model of the issue, I show that it can be welfare-improving to increase out-of-pocket expenditure on antibiotics used to tackle spreading infections.
The second chapter calculates the geographical distribution of people at risk of falling into poverty in the aftermath of droughts and floods in Malawi. Its methods can be expanded to identify the beneficiaries of scalable social safety nets or ex-ante climate insurance. Such programs would increase the resilience of the poor to climate change. The third chapter investigates the potential double dividend of internal migration in terms of poverty alleviation and forest regeneration in Central India. It relies on an innovative index of forest degradation created from high-resolution remote sensing imagery and unique data on internal migration and forest pressure based on a survey of 5,000 households.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/d8-dxqz-5535 |
| Date | January 2021 |
| Creators | Baquie, Sandra |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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