Abstract: While structural change is known to affect inequality, this paper demonstrates the reverse: changes in inequality can themselves trigger structural change, creating a bidirectional relationship. I develop a multisector model featuring skilled and unskilled labor, skill-biased technical change, nonhomothetic preferences, and consumption as a composite of market-bought and home-produced inputs. Inequality drives structural change through two mechanisms: a luxury/necessity channel and a marketization channel where home production is replaced by market services. Calibrating the model to U.S. data from 1980 to 2016, with demand parameters estimated from household data, I find that rising inequality accounts for 52% of the increase in high-skill services. For low-skill services, the two mechanisms work in opposite directions, partially offsetting each other. The leveling of inequality since the early 2000s reduced high-skill services growth by 24%, while declining tax progressivity increased the high-skill services share and decreased the low-skill services share.
Supported by a PhD research Grant from the research initiative 'Structural Transformation and Economic Growth' (STEG).
While my job market paper points to significant effects of inequality for structural change in the U.S., the quantitative relevance remains unclear for low- and middle-income countries. How important are the luxury/necessity and opportunity cost mechanisms in economies where the services share has substantial room to grow? A quantitative answer requires estimating demand parameters using micro and macro data from these countries, as preferences and home production patterns likely differ substantially from the U.S. experience. To this end, this project draws on sectoral expenditure data from the World Bank LSMS household modules for Tanzania and the Mexican National Survey of Household Income and Expenditure (ENIGH) as well as other data sources.
Sectoral heterogeneity in energy and emission intensities implies that structural transformation directly shapes aggregate energy demand and emissions. Simultaneously, carbon pricing induces sectoral reallocation by differentially affecting energy-intensive industries. Thus, structural change alters the economy's carbon footprint, while carbon taxes accelerate or redirect structural transformation itself. Key questions emerge: Does the shift toward modern energy-intensive services (data centers, AI, crypto) offset efficiency gains and reallocations from traditional manufacturing? Do carbon taxes reinforce or counteract underlying patterns of structural change? How does this feedback loop affect the optimal design of climate policy?