How do individuals behave in a society that rewards "merit", despite not being all on the same starting line? How should meritocracy be amended? This paper develops a model of career concerns in which agents publicly choose among several activities in which to exert effort, while differing along a privately observable char- acteristic ("head start") that affects their performance. The agents’ audience values talent, effort and head start. We highlight two contrasting effects: a displacement effect, dragging the "disfavored" (head start-wise) towards activities with lower in- centives on effort, and a distinction effect, pulling the "favored" towards activities with higher incentives.

I study the impact of the recent rise in remote work on households’ consumption, wealth and housing decisions, examining both short-run and long-run effects. Using detailed UK property-level housing data and a heterogeneous agent model with endogenous housing tenure and city geography, I show that remote work shifts households’ housing demand by increasing the demand for space and reducing the commuting costs. It affects where people live in the city and their housing wealth accumulation. The effects vary by access to remote work, income, and wealth. The rise in work-from-home can be compared to a suburb-wide gentrification shock as wealthy telecommuters opt for larger suburban homes, displacing marginal owners who turn to renting. In the long-run, work-from-home leads to the rise of a tele-premium and consumption inequality increases.

This paper shows that the within-country spatial distribution of colleges largely contributes to spatial inequalities. Using data on the universe of college applicants and programs in France, I document that higher education options are unevenly distributed across space while students’ demand is highly sensitive to geographic proximity. This creates inequalities in access to higher education across space, feeding gaps in educa- tional attainment and spatial skill sorting. To quantify these effects, I build a dynamic model linking equilibrium sorting on the higher education market and location choices of entry-level workers. I show that students’ and programs’ preferences can be identified and estimated from data on choices and equilibrium outcomes. One-third of regional gaps in educational attainment are explained by the interaction of the uneven distri- bution of colleges and mobility frictions. Eliminating the latter, however, generates a trade-off, as it benefits students from low-opportunity areas but accelerates their mi- gration to higher education hubs, magnifying regional inequalities. Low-opportunity areas could halt this brain drain and outsource the education of their local labor force by tying mobility scholarships with incentives to return.

Scale bias is the extent to which technical change increases the productivity of large relative to small firms. I show that this dimension of technical change is important for inequality. To illustrate the mechanism, I develop a tractable framework where people choose to work for wages or earn profits as entrepreneurs and where entrepreneurs choose from a set of available production technologies that differ in their fixed and marginal cost. Large-scale-biased technical change lowers entrepreneurship rates and increases top income inequality, primarily by concentrating business income. Small-scale-biased technical change does the opposite. I show the empirical relevance of scale bias by identifying the causal effects of adoption of two general purpose technologies that vary in scale bias, but are otherwise similar: steam engines (large-scale-biased) and electric motors (small-scale-biased). Using newly collected data from the United States and the Netherlands and a range of identification strategies, I show that these two technologies had opposite effects on firm sizes and inequality. Steam engines increased firm sizes and inequality, while electric motors decreased both. Consistent with scale bias (rather than skill bias), I find that adopting entrepreneurs were the main drivers of inequality increases after steam engine adoption.

Electricity production from wind and solar energy is projected to grow twelvefold until 2050. This paper studies the impact of renewable energy infrastructure on surrounding neighborhoods, its potential welfare costs for residents, and the implications for inequality. I focus on the wind energy expansion in Germany, 2000-2017. Using neighborhood data at 1-by-1 kilometer resolution and a novel IV strategy that exploits technology-induced changes in effective wind potential, I document that wind turbines decrease house prices and lead to residential sorting driven by the emigration of college-educated residents. Combined with a theory-consistent revealed preference argument, the reduced form results suggest that residents would be willing to pay between 0.9 and 1.4 percent of their income to avoid an additional wind turbine. I develop and estimate a quantitative spatial model in which wind turbines decrease amenities, residents can adapt, for example through sorting, and housing and labor markets respond in general equilibrium. The quantified model suggests that the disamenities from the total wind turbine expansion cost residents 0.83 percent of welfare or 31 billion USD. Allocating wind turbines to neighborhoods with low willingness- to-pay substantially reduces welfare costs but also places the burden on rural, poorer, and less educated regions. Finally, I discuss Germany’s wind development plans for 2030, and the implications for welfare and inequality.