Sanctions and misallocation. How sanctioned firms won and Russia lost

Using a unique natural experiment of staggered firm-level sanctions against Russia in 2014-2020 and the data on over 900,000 Russian firms, I estimate the effect of sanctions on targeted firms and on the aggregate economy. Surprisingly, sanctioned firms on average gained 38% more capital inputs after sanctions relative to the industry trends. The effect is in part driven by sanctioned state-owned firms, getting 60% more capital relative to non-sanctioned firms. Using additional data on subsidies and government contracts, I find that this result is explained by the government protection of targeted firms, that more than compensated for a negative sanctions shock. However, the sanctioned firms were already too large and had too much capital prior to sanctions. I use a heterogeneous firm framework to show that the distortions between sanctioned and non-sanctioned firms, which existed before the sanctions, got exacerbated after the joint effect of sanctions and government protection. I combine the causal estimates with the quantitative frame-work and estimate that on the aggregate, the Russian TFP dropped at least by 0.33% reaching 3% in relevant sectors

Essays in macro and development economics

This thesis studies the economics of firms, neighbourhoods, cities and regions in developing countries. It combines empirical and structural methods using satellite, administrative and survey data to study allocative efficiency, the economics of cities and urban planning in developing countries. The thesis is organised into four independent chapters. The first chapter studies the extent of misallocation among firms in Russia, and the role of state ownership in affecting allocative efficiency. I find that there are large wedges between state-owned and private firms reducing the aggregate TFP by at least 11%. Using a unique natural experiment of staggered firm-level US sanctions I find one channel through which resources become misallocated: the state-owned enterprises are shielded excessively from negative economic shocks. I find that allocative efficiency worsened after the sanctions episode and the Russian TFP dropped at least by 0.33% overall, reaching -3% in some sectors as a combined effect of sanctions and shielding. The second chapter explores new data methods and GIS tools for use in urban and spatial economics. It discusses the strengths and challenges in the application of novel datasets, such as satellite imagery, gridded population and develops methods that effectively apply these datasets to economic research questions. The third chapter (co-authored) explores the long-run consequences of planning and providing basic infrastructure in neighbourhoods, where people build their own homes. Using satellite images and surveys from the 2010s, we find that de novo planning induces neighbourhoods to develop better housing, with larger footprint areas, more stories, more connections to electricity and water, basic sanitation and access to roads. This effect remains even after accounting for selection of financially unconstrained owners into planned areas. The fourth chapter (co-authored) estimates urban agglomeration effects, exploring both simple and very nuanced measures of economic density to explain household income and wage differences across cities in six Sub-Saharan African countries. Defining cities consistently, we find large wage gains to being in denser cities in Sub-Saharan Africa, which are generally larger than such estimates for other parts of the world. We also find extraordinary household income gains to density that are far greater than wage ones. Such gains are consistent with the pull forces driving rapid urbanization in the region

Planning ahead for better neighborhoods: long-run evidence from Tanzania

Africa’s demand for urban housing is soaring, even as it faces a proliferation of slums. In this setting, can modest infrastructure investments in greenfield areas where people subsequently build their own houses facilitate long-run neighborhood development? We study Sites and Services projects implemented in seven Tanzanian cities during the 1970s and 1980s, and we use a spatial regression discontinuity design to compare greenfield areas that were treated (de novo) with nearby greenfield areas that were not. We find that by the 2010s, de novo areas developed into neighborhoods with larger, more regularly laid-out buildings and better-quality housing

Planning ahead for better neighborhoods: long run evidence from Tanzania

What are the long run consequences of planning and providing basic infrastructure in neighborhoods, where people build their own homes? We study "Sites and Services" projects implemented in seven Tanzanian cities during the 1970s and 1980s, half of which provided infrastructure in previously unpopulated areas (de novo neighborhoods), while the other half upgraded squatter settlements. Using satellite images and surveys from the 2010s, we find that de novo neighborhoods developed better housing than adjacent residential areas (control areas) that were also initially unpopulated. Specifically, de novo neighborhood are more orderly and their buildings have larger footprint areas and are more likely to have multiple stories, as well as connections to electricity and water, basic sanitation and access to roads. And though de novo neighborhoods generally attracted better educated residents than control areas, the educational difference is too small to account for the large difference in residential quality that we find. While we have no natural counterfactual for the upgrading areas, descriptive evidence suggests that they are if anything worse than the control area

Planning ahead for better neighborhoods: long-run evidence from Tanzania

Africa’s demand for urban housing is soaring, even as it faces a proliferation of slums. In this setting, can modest infrastructure investments in greenfield areas where people subsequently build their own houses facilitate long-run neighborhood development? We study Sites and Services projects implemented in seven Tanzanian cities during the 1970s and 1980s, and we use a spatial regression discontinuity design to compare greenfield areas that were treated (de novo) with nearby greenfield areas that were not. We find that by the 2010s, de novo areas developed into neighborhoods with larger, more regularly laid-out buildings and better-quality housing