Climate change is expected to have heterogeneous effect on economic sectors across geographic locations. India is the seventh largest country in area with second largest population in the world, and carries the seventh highest risk to climate change in the world. Assessing potential decisions of economic agents when adapting to climate change is challenging but crucial for policy making. Climate Change could lead workers to sort out of climate exposed sectors (Heat stressed industries - agriculture, manufacturing, mining and construction). They could possibly migrate to other geographic locations. However if firms have higher capacity to stomach productivity shock and can adapt faster than workers, then it would alter workers' decisions. In this paper, I develop a dynamic spatial equilibrium model to quantify economic agents' response to climate change and estimate its effect on labor reallocation, population displacement, and productivity change for firms. By simulating the model for future policy scenarios, I estimate the aggregate distributional impacts of climate change in terms of trade off between adaptation through sectoral shift and migration. The field so far has followed either of the two strings of literature - either a partial equilibrium approach like Deschênes and Greenstone, 2007 or macroeconomic climate models like Nordhaus, 1992. This paper contributes to a nascent body of literature which combines detailed micro data with quantitative macroeconomic models to study economics of climate change adaptation (Balboni, 2019; Rudik et al, 2021; Nath, 2022; Conte, 2022; Cruz, 2021). To answer this question, I create a panel dataset of Indian districts spanning 1987-2009 using National Sample survey data. The dataset is novel as a crosswalk of districts and industry concordance over this time period did not exist before. Data includes share of labor in each sector, average monthly expenditure and expenditure on food, clothing, durables, goods and services. I combine this data with climate variables which I get from Version 3 of the Global Meteorological Forcing Dataset (GMFCD) produced at Princeton University.