Essays on Technological Change and Scientific Research

The primary objective of economic science has long been to understand and promote growth, as it contributes to a more prosperous civilization and improves individual well-being. The core drivers of growth—capital accumulation, human capital development, and technological progress—are well-established. While capital deepening enables scaling of production, and human capital enhances labor productivity, technological and scientific advancements remain the most critical, as they continually redefine the efficiency frontier. Given this centrality, it is essential to understand how technological progress affects labor markets, how research resources should be allocated, and how technology relates to human capital formation. The first part of this dissertation examines the impact of technological improvement on the labor market. Standard models based on constant elasticity of substitution (CES) production functions suggest that better technology benefits all types of labor while widening wage inequality. However, empirical evidence indicates that substitution elasticities vary over time and across countries. To address this, we employ an iso-elastic production function, where elasticity evolves with technological progress. We demonstrate that wages may respond non-monotonically to technological change. Integrating this framework with a sectoral model and a self-selective labor force, we explain sectoral transitions and “sectoral blockages”—cases where automation in one sector is not matched by sufficient progress in adjacent sectors—resulting in rising unemployment and falling wages. The second part explores how fluctuations in science funding affect research output and human capital formation through mentoring. While prior work documents the adverse effects of budget cuts, little is known about the consequences of unsustained funding spikes. Between 1998 and 2002, the U.S. National Institutes of Health (NIH) budget doubled before returning to near-previous levels. Using this natural experiment, and datasets such as NIH ExPORTER, ProQuest, and Author-ity, we apply a staggered difference-in-differences approach at the principal investigator level. We find that the budget increase boosted output quantity but degraded output quality—including the quality of mentored students. The third part offers a conceptual explanation for these findings. Using an overlapping generations model, we simulate the dynamics between junior and senior researchers producing knowledge. We show that in a regime shift from constrained to surplus funding, the initial gain in quantity is accompanied by a decline in quality. When funding reverts to previous levels, both quality and quantity fall, leading to overall mitigated outcomes. These results suggest that steady, linear budget increases may be more effective than short-term spikes in maximizing long-term research productivity and quality.