Tianyu Fan

Structural transformation in most currently developing countries takes the form of a rapid rise in services but limited industrialization. In this paper, we propose a new methodology to structurally estimate productivity growth in service industries that circumvents the notorious difficulties in measuring quality improvements. In our theory, the expansion of the service sector is both a consequence, due to income effects, and a cause, due to productivity growth, of the development process. We estimate the model using Indian household data. We find that productivity growth in nontradable consumer services such as retail, restaurants, or residential real estate was an important driver of structural transformation and rising living standards between 1987 and 2011. However, the welfare gains were heavily skewed toward high-income urban dwellers.

We introduce an event-based measure of bilateral geopolitical alignment from 373,020 events across 193 countries over 1960–2024, compiled using large language models and validated with external benchmarks and a human audit. Aggregating alignment with major nations summarizes each country's external geopolitical environment. In local projections using within-country variation, a sustained one-standard-deviation improvement in this environment is followed by about 10 percent higher GDP per capita after 25 years. This relationship is similar across major-nation partners and historical periods, and is accompanied by greater stability, investment, trade openness, and productivity. Accounting exercises map observed realignments into substantial GDP differences.

We show that since the mid-1990s, the trade-promoting effects of tariff liberalization have been increasingly offset by deteriorating geopolitical alignment, slowing trade globalization after 2007. To quantify this barrier, we use large language models to compile 833,485 geopolitical events across 193 countries, 1950–2024, and construct a bilateral geopolitical alignment score. Using local projections, we estimate that a one-standard-deviation permanent improvement in alignment raises bilateral trade by 22 percent in the long run. In an Armington framework, tariff reductions raised 2021 global trade by about 7.5 percent, while geopolitical deterioration reduced it by about 5.3 percent, with uneven welfare effects.

This paper develops a framework for empirically estimating aggregate labor supply across occupations with flexible substitution patterns and applies it to study the incidence of automation and artificial intelligence in the U.S. labor market. Central to the analysis is the distance-dependent elasticity of substitution (DIDES), where worker substitutability between occupations declines with their distance in skill space. By mapping 306 occupations into cognitive, manual, and interpersonal skill dimensions, we estimate a low-dimensional latent skill model that preserves granular substitution patterns. We show that both automation and artificial intelligence cluster in skill-adjacent occupations, constraining employment adjustment and amplifying wage effects: 20–50% of labor demand shocks pass through to wages, while mobility recovers only 20% of wage losses.