What are the causes and consequences of the spatial variation of economic activities both within and across cities? To contribute to our understanding of this question, the two chapters in this PhD thesis seek to advance two research agendas. The first is an understanding of the causes of the spatial variation in structural transformation in a country and how this affects spatial inequality and the upward income mobility of workers. The second is addressing the mechanisms that shape the internal structure of a city in a developing country. The approach to both is a mix of theory and empirics, leveraging the structure of the model for identification. The first chapter develops a dynamic overlapping generations model of economic geography to explain variation in structural transformation across space and time. Despite the heterogeneity across locations, sectors, and time, the model remains tractable and is calibrated to match metropolitan area data for the U.S. economy from 1980 to 2010. The calibration allows us to back out measures of upward mobility and inequality, thereby providing theoretical underpinnings to the geographical variation of upward mobility and spatial inequality. The counterfactual analysis shows that structural transformation in the last decades has had substantial effects on mobility. The second chapter studies how quantitative urban models can be calibrated in the data-sparse environments in developing countries using data from Dhaka. In particular, this paper shows how newly available satellite data on building heights can be used to estimate the housing supply elasticity. With the model parameters, we can also estimate the price of land and floor space in the city, which are prices that are usually difficult to observe for cities in developing countries directly. This paper also presents model counterfactuals to illustrate how essential it is to understand the general equilibrium impacts of the policy change
